diff --git a/R-package/R/lgb.Predictor.R b/R-package/R/lgb.Predictor.R
index 26b70d778d40..7a709e5dd20b 100644
--- a/R-package/R/lgb.Predictor.R
+++ b/R-package/R/lgb.Predictor.R
@@ -219,6 +219,15 @@ Predictor <- R6::R6Class(
         preds <- matrix(preds, ncol = npred_per_case, byrow = TRUE)
       }
 
+      # Keep row names if possible
+      if (NROW(row.names(data)) && NROW(data) == NROW(preds)) {
+        if (is.null(dim(preds))) {
+          names(preds) <- row.names(data)
+        } else {
+          row.names(preds) <- row.names(data)
+        }
+      }
+
       return(preds)
     }
 
diff --git a/R-package/tests/testthat/test_Predictor.R b/R-package/tests/testthat/test_Predictor.R
index 2057324d07fd..19360daa688f 100644
--- a/R-package/tests/testthat/test_Predictor.R
+++ b/R-package/tests/testthat/test_Predictor.R
@@ -2,6 +2,8 @@ VERBOSITY <- as.integer(
   Sys.getenv("LIGHTGBM_TEST_VERBOSITY", "-1")
 )
 
+library(Matrix)
+
 test_that("Predictor$finalize() should not fail", {
     X <- as.matrix(as.integer(iris[, "Species"]), ncol = 1L)
     y <- iris[["Sepal.Length"]]
@@ -112,6 +114,115 @@ test_that("start_iteration works correctly", {
     expect_equal(pred_leaf1, pred_leaf2)
 })
 
+.expect_has_row_names <- function(pred, X) {
+    if (is.vector(pred)) {
+        rnames <- names(pred)
+    } else {
+        rnames <- row.names(pred)
+    }
+    expect_false(is.null(rnames))
+    expect_true(is.vector(rnames))
+    expect_true(length(rnames) > 0L)
+    expect_equal(row.names(X), rnames)
+}
+
+.expect_doesnt_have_row_names <- function(pred) {
+    if (is.vector(pred)) {
+        expect_null(names(pred))
+    } else {
+        expect_null(row.names(pred))
+    }
+}
+
+.check_all_row_name_expectations <- function(bst, X) {
+
+    # dense matrix with row names
+    pred <- predict(bst, X)
+    .expect_has_row_names(pred, X)
+    pred <- predict(bst, X, rawscore = TRUE)
+    .expect_has_row_names(pred, X)
+    pred <- predict(bst, X, predleaf = TRUE)
+    .expect_has_row_names(pred, X)
+    pred <- predict(bst, X, predcontrib = TRUE)
+    .expect_has_row_names(pred, X)
+
+    # dense matrix without row names
+    Xcopy <- X
+    row.names(Xcopy) <- NULL
+    pred <- predict(bst, Xcopy)
+    .expect_doesnt_have_row_names(pred)
+
+    # sparse matrix with row names
+    Xcsc <- as(X, "CsparseMatrix")
+    pred <- predict(bst, Xcsc)
+    .expect_has_row_names(pred, Xcsc)
+    pred <- predict(bst, Xcsc, rawscore = TRUE)
+    .expect_has_row_names(pred, Xcsc)
+    pred <- predict(bst, Xcsc, predleaf = TRUE)
+    .expect_has_row_names(pred, Xcsc)
+    pred <- predict(bst, Xcsc, predcontrib = TRUE)
+    .expect_has_row_names(pred, Xcsc)
+
+    # sparse matrix without row names
+    Xcopy <- Xcsc
+    row.names(Xcopy) <- NULL
+    pred <- predict(bst, Xcopy)
+    .expect_doesnt_have_row_names(pred)
+}
+
+test_that("predict() keeps row names from data (regression)", {
+    data("mtcars")
+    X <- as.matrix(mtcars[, -1L])
+    y <- as.numeric(mtcars[, 1L])
+    dtrain <- lgb.Dataset(
+      X
+      , label = y
+      , params = list(
+        max_bins = 5L
+        , min_data_in_bin = 1L
+      )
+    )
+    bst <- lgb.train(
+        data = dtrain
+        , obj = "regression"
+        , nrounds = 5L
+        , verbose = VERBOSITY
+        , params = list(min_data_in_leaf = 1L)
+    )
+    .check_all_row_name_expectations(bst, X)
+})
+
+test_that("predict() keeps row names from data (binary classification)", {
+    data(agaricus.train, package = "lightgbm")
+    X <- as.matrix(agaricus.train$data)
+    y <- agaricus.train$label
+    row.names(X) <- paste("rname", seq(1L, nrow(X)), sep = "")
+    dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))
+    bst <- lgb.train(
+        data = dtrain
+        , obj = "binary"
+        , nrounds = 5L
+        , verbose = VERBOSITY
+    )
+    .check_all_row_name_expectations(bst, X)
+})
+
+test_that("predict() keeps row names from data (multi-class classification)", {
+    data(iris)
+    y <- as.numeric(iris$Species) - 1.0
+    X <- as.matrix(iris[, names(iris) != "Species"])
+    row.names(X) <- paste("rname", seq(1L, nrow(X)), sep = "")
+    dtrain <- lgb.Dataset(X, label = y, params = list(max_bins = 5L))
+    bst <- lgb.train(
+        data = dtrain
+        , obj = "multiclass"
+        , params = list(num_class = 3L)
+        , nrounds = 5L
+        , verbose = VERBOSITY
+    )
+    .check_all_row_name_expectations(bst, X)
+})
+
 test_that("predictions for regression and binary classification are returned as vectors", {
     data(mtcars)
     X <- as.matrix(mtcars[, -1L])