diff --git a/.github/workflows/Tests.yml b/.github/workflows/Tests.yml
index d8f505bed..31d8df00f 100644
--- a/.github/workflows/Tests.yml
+++ b/.github/workflows/Tests.yml
@@ -37,6 +37,7 @@ jobs:
           - "LinearSolvePETSc"
           - "LinearSolvePardiso"
           - "LinearSolveGinkgo"
+          - "LinearSolveElemental"
           - "NoPre"
           - "LinearSolveAutotune"
           - "LinearSolvePyAMG"
@@ -59,6 +60,9 @@ jobs:
           # Ginkgo.jl is not available on Windows
           - group: LinearSolveGinkgo
             os: windows-latest
+          # Elemental_jll has no Windows binary
+          - group: LinearSolveElemental
+            os: windows-latest
     uses: "SciML/.github/.github/workflows/tests.yml@v1"
     with:
       group: "${{ matrix.group }}"
diff --git a/Project.toml b/Project.toml
index 25ddebb54..931ed6048 100644
--- a/Project.toml
+++ b/Project.toml
@@ -36,6 +36,7 @@ CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
 CUDSS = "45b445bb-4962-46a0-9369-b4df9d0f772e"
 CUSOLVERRF = "a8cc9031-bad2-4722-94f5-40deabb4245c"
 CliqueTrees = "60701a23-6482-424a-84db-faee86b9b1f8"
+Elemental = "902c3f28-d1ec-5e7e-8399-a24c3845ee38"
 EnzymeCore = "f151be2c-9106-41f4-ab19-57ee4f262869"
 FastAlmostBandedMatrices = "9d29842c-ecb8-4973-b1e9-a27b1157504e"
 FastLapackInterface = "29a986be-02c6-4525-aec4-84b980013641"
@@ -66,6 +67,7 @@ LinearSolveCUDAExt = "CUDA"
 LinearSolveCUDSSExt = "CUDSS"
 LinearSolveCUSOLVERRFExt = ["CUSOLVERRF", "SparseArrays"]
 LinearSolveCliqueTreesExt = ["CliqueTrees", "SparseArrays"]
+LinearSolveElementalExt = "Elemental"
 LinearSolveEnzymeExt = ["EnzymeCore", "SparseArrays"]
 LinearSolveFastAlmostBandedMatricesExt = "FastAlmostBandedMatrices"
 LinearSolveFastLapackInterfaceExt = "FastLapackInterface"
@@ -100,6 +102,7 @@ CliqueTrees = "1.13.1"
 ComponentArrays = "0.15.29"
 ConcreteStructs = "0.2.3"
 DocStringExtensions = "0.9.3"
+Elemental = "0.6"
 EnumX = "1.0.4"
 EnzymeCore = "0.8.5"
 ExplicitImports = "1.10"
@@ -159,6 +162,7 @@ BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
 BlockDiagonals = "0a1fb500-61f7-11e9-3c65-f5ef3456f9f0"
 CliqueTrees = "60701a23-6482-424a-84db-faee86b9b1f8"
 ComponentArrays = "b0b7db55-cfe3-40fc-9ded-d10e2dbeff66"
+Elemental = "902c3f28-d1ec-5e7e-8399-a24c3845ee38"
 ExplicitImports = "7d51a73a-1435-4ff3-83d9-f097790105c7"
 FastAlmostBandedMatrices = "9d29842c-ecb8-4973-b1e9-a27b1157504e"
 FastLapackInterface = "29a986be-02c6-4525-aec4-84b980013641"
@@ -188,4 +192,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [targets]
-test = ["AlgebraicMultigrid", "Aqua", "Test", "IterativeSolvers", "InteractiveUtils", "KrylovKit", "KrylovPreconditioners", "Pkg", "Random", "SafeTestsets", "MultiFloats", "ForwardDiff", "HYPRE", "MPI", "PETSc", "BlockDiagonals", "FiniteDiff", "BandedMatrices", "FastAlmostBandedMatrices", "StaticArrays", "AllocCheck", "StableRNGs", "Zygote", "RecursiveFactorization", "Sparspak", "CliqueTrees", "FastLapackInterface", "SparseArrays", "ExplicitImports", "ComponentArrays"]
+test = ["AlgebraicMultigrid", "Aqua", "Test", "IterativeSolvers", "InteractiveUtils", "KrylovKit", "KrylovPreconditioners", "Pkg", "Random", "SafeTestsets", "MultiFloats", "ForwardDiff", "HYPRE", "MPI", "PETSc", "BlockDiagonals", "FiniteDiff", "BandedMatrices", "FastAlmostBandedMatrices", "StaticArrays", "AllocCheck", "StableRNGs", "Zygote", "RecursiveFactorization", "Sparspak", "CliqueTrees", "FastLapackInterface", "SparseArrays", "ExplicitImports", "ComponentArrays", "Elemental"]
diff --git a/ext/LinearSolveElementalExt.jl b/ext/LinearSolveElementalExt.jl
new file mode 100644
index 000000000..bbc5a77fd
--- /dev/null
+++ b/ext/LinearSolveElementalExt.jl
@@ -0,0 +1,88 @@
+module LinearSolveElementalExt
+
+using LinearSolve, Elemental, LinearAlgebra
+using LinearSolve: LinearCache, LinearVerbosity, OperatorAssumptions
+using SciMLBase: SciMLBase, ReturnCode
+
+const ElementalNumeric = Union{Float32, Float64, ComplexF32, ComplexF64}
+
+function _elemental_eltype(A)
+    T = eltype(A)
+    return T <: ElementalNumeric ? T : (T <: Complex ? ComplexF64 : Float64)
+end
+
+function _to_elemental_matrix(A::Base.AbstractVecOrMat, ::Type{T}) where {T}
+    return convert(Elemental.Matrix{T}, Matrix{T}(A))
+end
+
+# Always copy: lu!/qr!/cholesky! are in-place. Returning A directly would
+# corrupt the user's matrix and cause reinit! + re-solve to factorize garbage.
+function _to_elemental_matrix(A::Elemental.Matrix, ::Type{T}) where {T}
+    B = Elemental.Matrix(T)
+    Elemental.resize!(B, size(A, 1), size(A, 2))
+    copyto!(B, A)
+    return B
+end
+
+function _b_to_elemental(b::AbstractVector, ::Type{T}) where {T}
+    return convert(Elemental.Matrix{T}, reshape(Vector{T}(b), length(b), 1))
+end
+
+function _b_to_elemental(b::Elemental.Matrix{T}, ::Type{T}) where {T}
+    return b
+end
+
+function _b_to_elemental(b::Elemental.Matrix, ::Type{T}) where {T}
+    return convert(Elemental.Matrix{T}, convert(Base.Matrix{T}, b))
+end
+
+function _elemental_factorize(alg::ElementalJL, A_el::Elemental.Matrix)
+    if alg.method === :LU
+        return LinearAlgebra.lu!(A_el)
+    elseif alg.method === :QR
+        return LinearAlgebra.qr!(A_el)
+    elseif alg.method === :Cholesky
+        # Must call cholesky!(A_el) directly, not cholesky!(Hermitian(A_el)).
+        # The Hermitian path returns LinearAlgebra.Cholesky whose .factors is a
+        # raw Elemental.Matrix with no \ defined, silently falling back to Julia's
+        # generic LU on already-destroyed data. The direct path returns
+        # CholeskyMatrix{T} which has \ → ElSolveAfterCholesky.
+        return LinearAlgebra.cholesky!(A_el)
+    else
+        error(
+            "Unknown method $(alg.method) for ElementalJL. " *
+                "Valid choices are :LU (default), :QR, or :Cholesky."
+        )
+    end
+end
+
+function LinearSolve.init_cacheval(
+        alg::ElementalJL, A, b, u, Pl, Pr, maxiters::Int, abstol, reltol,
+        verbose::Union{LinearVerbosity, Bool}, assumptions::OperatorAssumptions
+    )
+    # Must return a properly typed factorization so that LinearCache is
+    # parameterized with the correct cacheval type.  solve! will re-factorize
+    # immediately (isfresh = true on the first call) and overwrite this value.
+    T = _elemental_eltype(A)
+    A_el = _to_elemental_matrix(A, T)
+    return _elemental_factorize(alg, A_el)
+end
+
+function SciMLBase.solve!(cache::LinearCache, alg::ElementalJL; kwargs...)
+    A = cache.A
+    T = _elemental_eltype(A)
+
+    if cache.isfresh
+        A_el = _to_elemental_matrix(A, T)
+        cache.cacheval = _elemental_factorize(alg, A_el)
+        cache.isfresh = false
+    end
+
+    fact = LinearSolve.@get_cacheval(cache, :ElementalJL)
+    x_jl = convert(Base.Matrix{T}, fact \ _b_to_elemental(cache.b, T))
+    copyto!(cache.u, vec(x_jl))
+
+    return SciMLBase.build_linear_solution(alg, cache.u, nothing, cache; retcode = ReturnCode.Success)
+end
+
+end # module
diff --git a/src/LinearSolve.jl b/src/LinearSolve.jl
index 8e65f41c8..0c3c06d6c 100644
--- a/src/LinearSolve.jl
+++ b/src/LinearSolve.jl
@@ -523,6 +523,8 @@ export KrylovJL, KrylovJL_CG, KrylovJL_MINRES, KrylovJL_GMRES,
     IterativeSolversJL_BICGSTAB, IterativeSolversJL_MINRES, IterativeSolversJL_IDRS,
     KrylovKitJL, KrylovKitJL_CG, KrylovKitJL_GMRES, KrylovJL_MINARES, AlgebraicMultigridJL
 
+export ElementalJL
+
 export GinkgoJL, GinkgoJL_CG, GinkgoJL_GMRES
 
 export SimpleGMRES
diff --git a/src/extension_algs.jl b/src/extension_algs.jl
index 0c63bb6b4..832e9207c 100644
--- a/src/extension_algs.jl
+++ b/src/extension_algs.jl
@@ -1241,3 +1241,69 @@ struct ParUFactorization <: AbstractSparseFactorization
         return new(reuse_symbolic)
     end
 end
+
+"""
+    ElementalJL(; method = :LU)
+
+A wrapper for [Elemental.jl](https://github.com/JuliaParallel/Elemental.jl),
+providing distributed-memory dense linear algebra solvers built on the
+[Elemental](https://github.com/elemental/Elemental) C++ library by Jack Poulson.
+(LLNL maintains an active GPU-focused fork of Elemental called
+[Hydrogen](https://github.com/LLNL/Elemental).)
+
+## Keyword Arguments
+
+  - `method`: The factorization method to use. Options:
+    - `:LU` (default) — LU factorization with partial pivoting. Suitable for
+      general square systems.
+    - `:QR` — QR factorization. Suitable for square or overdetermined systems.
+    - `:Cholesky` — Cholesky factorization. Requires the matrix to be Hermitian
+      positive definite.
+
+## Supported Element Types
+
+`Float32`, `Float64`, `ComplexF32`, `ComplexF64`. Matrices with other element
+types are promoted to `Float64` (real) or `ComplexF64` (complex) before being
+passed to Elemental.
+
+## Notes
+
+  - Serial `Elemental.Matrix` values are accepted directly as the problem
+    matrix `A`; they are copied before factorization so the original is
+    never mutated.
+  - When `A` is a standard Julia `AbstractMatrix`, it is copied into an
+    `Elemental.Matrix` for the factorization.
+  - The factorization is cached across repeated solves with the same matrix
+    (i.e. when `isfresh = false`).
+
+!!! note
+
+    Using this solver requires adding the package Elemental.jl:
+    ```julia
+    using Elemental
+    ```
+    Elemental.jl automatically initialises MPI when loaded; no explicit
+    `MPI.Init()` call is needed for serial usage.
+
+## Example
+
+```julia
+using LinearSolve, Elemental
+
+A = rand(100, 100); A = A + A' + 100I  # well-conditioned
+b = rand(100)
+prob = LinearProblem(A, b)
+
+# LU (default)
+sol = solve(prob, ElementalJL())
+# Cholesky (symmetric positive definite)
+sol = solve(prob, ElementalJL(method = :Cholesky))
+```
+"""
+struct ElementalJL <: AbstractDenseFactorization
+    method::Symbol
+end
+
+function ElementalJL(; method::Symbol = :LU)
+    return ElementalJL(method)
+end
diff --git a/test/elemental/Project.toml b/test/elemental/Project.toml
new file mode 100644
index 000000000..167c67c40
--- /dev/null
+++ b/test/elemental/Project.toml
@@ -0,0 +1,11 @@
+[deps]
+Elemental = "902c3f28-d1ec-5e7e-8399-a24c3845ee38"
+LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
+LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
+Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+SciMLBase = "0bca4576-84f4-4d90-8ffe-ffa030f20462"
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+
+[compat]
+Elemental = "0.6"
+julia = "1.10"
diff --git a/test/elemental/elemental.jl b/test/elemental/elemental.jl
new file mode 100644
index 000000000..f4421d3ac
--- /dev/null
+++ b/test/elemental/elemental.jl
@@ -0,0 +1,74 @@
+using LinearSolve, LinearAlgebra, Test, Random, SciMLBase
+using Elemental
+
+@testset "ElementalJL" begin
+    rng = Random.MersenneTwister(42)
+    n = 50
+
+    # ── General square Float64 system ─────────────────────────────────────────
+    A_el = Matrix{Float64}(I, n, n) + 0.1 * rand(rng, n, n)
+    b_el = rand(rng, n)
+    prob_el = LinearProblem(A_el, b_el)
+
+    # Default method: LU
+    sol_lu = solve(prob_el, ElementalJL())
+    @test A_el * sol_lu.u ≈ b_el atol = 1.0e-8
+    @test sol_lu.retcode == ReturnCode.Success
+
+    # QR factorization
+    sol_qr = solve(prob_el, ElementalJL(method = :QR))
+    @test A_el * sol_qr.u ≈ b_el atol = 1.0e-8
+
+    # ── Symmetric positive definite system → Cholesky ─────────────────────────
+    B_spd = rand(rng, n, n)
+    A_spd = B_spd * B_spd' + n * I  # guaranteed SPD
+    b_spd = rand(rng, n)
+    sol_ch = solve(LinearProblem(A_spd, b_spd), ElementalJL(method = :Cholesky))
+    @test A_spd * sol_ch.u ≈ b_spd atol = 1.0e-8
+
+    # ── Float32 system (all 4 eltypes are supported by Elemental_jll) ──────────
+    A_f32 = Matrix{Float32}(I, n, n) + 0.1f0 * rand(rng, Float32, n, n)
+    b_f32 = rand(rng, Float32, n)
+    sol_f32 = solve(LinearProblem(A_f32, b_f32), ElementalJL())
+    @test A_f32 * sol_f32.u ≈ b_f32 atol = 1.0f-4
+
+    # ── Complex system ─────────────────────────────────────────────────────────
+    A_cplx = Matrix{ComplexF64}(I, n, n) + 0.1 * (rand(rng, n, n) + im * rand(rng, n, n))
+    b_cplx = rand(rng, ComplexF64, n)
+    sol_cplx = solve(LinearProblem(A_cplx, b_cplx), ElementalJL())
+    @test A_cplx * sol_cplx.u ≈ b_cplx atol = 1.0e-8
+
+    # ── Pass an Elemental.Matrix directly as A ────────────────────────────────
+    A_emat = convert(Elemental.Matrix{Float64}, A_el)
+    b_emat = rand(rng, n)
+    sol_emat = solve(LinearProblem(A_emat, b_emat), ElementalJL())
+    @test A_el * sol_emat.u ≈ b_emat atol = 1.0e-8
+
+    # ── Elemental.Matrix as A: re-factorization must not use corrupted data ───
+    # Regression: _to_elemental_matrix must copy A. If it returned A directly,
+    # lu! would factorize in-place; the second reinit!(A=...) + solve! would
+    # re-factorize already-destroyed data and produce a wrong result.
+    A_emat2 = convert(Elemental.Matrix{Float64}, A_el)
+    b_emat2a = rand(rng, n)
+    b_emat2b = rand(rng, n)
+    cache_emat = SciMLBase.init(LinearProblem(A_emat2, b_emat2a), ElementalJL())
+    solve!(cache_emat)
+    @test A_el * cache_emat.u ≈ b_emat2a atol = 1.0e-8
+    # Pass A= to reinit! so isfresh=true, forcing _to_elemental_matrix to run again.
+    reinit!(cache_emat; A = A_emat2, b = b_emat2b)
+    solve!(cache_emat)
+    @test A_el * cache_emat.u ≈ b_emat2b atol = 1.0e-8
+
+    # ── Cache reuse (same A, different b) ─────────────────────────────────────
+    b_el2 = rand(rng, n)
+    cache = SciMLBase.init(prob_el, ElementalJL())
+    solve!(cache)
+    @test A_el * cache.u ≈ b_el atol = 1.0e-8
+
+    reinit!(cache; b = b_el2)
+    solve!(cache)
+    @test A_el * cache.u ≈ b_el2 atol = 1.0e-8
+
+    # ── Unknown method raises an error ────────────────────────────────────────
+    @test_throws ErrorException solve(prob_el, ElementalJL(method = :Bogus))
+end
diff --git a/test/resolve.jl b/test/resolve.jl
index c7a5e156a..2a02695e2 100644
--- a/test/resolve.jl
+++ b/test/resolve.jl
@@ -51,6 +51,10 @@ for alg in vcat(
             (
             !(alg == ParUFactorization) ||
                 Base.get_extension(LinearSolve, :LinearSolveParUExt) !== nothing
+        ) &&
+            (
+            !(alg == ElementalJL) ||
+                Base.get_extension(LinearSolve, :LinearSolveElementalExt) !== nothing
         )
         A = [1.0 2.0; 3.0 4.0]
         alg in [
diff --git a/test/runtests.jl b/test/runtests.jl
index df24888c0..f18e973c5 100644
--- a/test/runtests.jl
+++ b/test/runtests.jl
@@ -98,6 +98,13 @@ if !Base.Sys.iswindows() && GROUP == "LinearSolveGinkgo"
     @time @safetestset "Ginkgo" include("ginkgo/ginkgo.jl")
 end
 
+if !Base.Sys.iswindows() && GROUP == "LinearSolveElemental"
+    Pkg.activate("elemental")
+    Pkg.develop(PackageSpec(path = dirname(@__DIR__)))
+    Pkg.instantiate()
+    @time @safetestset "Elemental" include("elemental/elemental.jl")
+end
+
 if Base.Sys.islinux() && (GROUP == "All" || GROUP == "LinearSolveHYPRE") && HAS_EXTENSIONS
     @time @safetestset "LinearSolveHYPRE" include("hypretests.jl")
 end