diff --git a/src/interface_r/R/model.R b/src/interface_r/R/model.R
index 2116502ca..7173f4f40 100644
--- a/src/interface_r/R/model.R
+++ b/src/interface_r/R/model.R
@@ -73,6 +73,18 @@ print.h2o4gpu_model <- function(x, ...) {
 #' @param ... Additional arguments (unused for now).
 #' 
 #' @export
+#' @examples 
+#' \dontrun{
+#' 
+#' library(h2o4gpu)
+#' 
+#' # Setup dataset
+#' x <- iris[1:4]
+#' y <- as.integer(iris$Species) - 1
+#' 
+#' # Train the classifier
+#' h2o4gpu.random_forest_classifier() %>% fit(x, y)
+#' }
 fit.h2o4gpu_model <- function(object, x, y = NULL, ...) {
   if (inherits(object$model, "h2o4gpu.solvers.elastic_net.ElasticNet") && object$params$family == "logistic"){
     if (length(unique(y)) > 2){
@@ -94,6 +106,23 @@ fit.h2o4gpu_model <- function(object, x, y = NULL, ...) {
 #' @param type One of "raw" or "prob", indicating the type of output: predicted values or probabilities
 #' @param ... Additional arguments (unused for now).
 #' @export
+#' @examples 
+#' \dontrun{
+#' 
+#' library(h2o4gpu)
+#' 
+#' # Setup dataset
+#' x <- iris[1:4]
+#' y <- as.integer(iris$Species) - 1
+#' 
+#' # Initialize and train the classifier
+#' model <- h2o4gpu.random_forest_classifier() %>% fit(x, y)
+#' 
+#' # Make predictions
+#' predictions <- model %>% predict(x)
+#' 
+#' }
+#' 
 predict.h2o4gpu_model <- function(object, x, type="raw", ...) {
   if (type == "raw") {
     preds <- object$model$predict(X = resolve_model_input(x), ...)
@@ -122,6 +151,27 @@ predict.h2o4gpu_model <- function(object, x, type="raw", ...) {
 #' be used in generating predictions.
 #' @param ... Additional arguments (unused for now).
 #' @export
+#' @examples 
+#' \dontrun{
+#' 
+#' library(h2o4gpu)
+#' 
+#' # Prepare data
+#' iris$Species <- as.integer(iris$Species) # convert to numeric data
+#' 
+#' # Randomly sample 80% of the rows for the training set
+#' set.seed(1)
+#' train_idx <- sample(1:nrow(iris), 0.8*nrow(iris)) 
+#' train <- iris[train_idx, ]
+#' test <- iris[-train_idx, ]
+#' 
+#' # Train a K-Means model
+#' model_km <- h2o4gpu.kmeans(n_clusters = 3L) %>% fit(train)
+#' 
+#' # Transform test data
+#' test_dist <- model_km %>% transform(test)
+#' 
+#' }
 transform.h2o4gpu_model <- function(object, x, ...) {
   object$model$transform(X = resolve_model_input(x), ...)
 }
diff --git a/src/interface_r/man/fit.h2o4gpu_model.Rd b/src/interface_r/man/fit.h2o4gpu_model.Rd
index 9d79ea098..ae7988441 100644
--- a/src/interface_r/man/fit.h2o4gpu_model.Rd
+++ b/src/interface_r/man/fit.h2o4gpu_model.Rd
@@ -22,3 +22,16 @@ specified as \code{NULL}.}
 \description{
 This function builds the model using the training data specified.
 }
+\examples{
+\dontrun{
+
+library(h2o4gpu)
+
+# Setup dataset
+x <- iris[1:4]
+y <- as.integer(iris$Species) - 1
+
+# Train the classifier
+h2o4gpu.random_forest_classifier() \%>\% fit(x, y)
+}
+}
diff --git a/src/interface_r/man/predict.h2o4gpu_model.Rd b/src/interface_r/man/predict.h2o4gpu_model.Rd
index 5b76245bb..d5b3fa2ba 100644
--- a/src/interface_r/man/predict.h2o4gpu_model.Rd
+++ b/src/interface_r/man/predict.h2o4gpu_model.Rd
@@ -20,3 +20,21 @@ be used in generating predictions.}
 This function makes predictions from new data using a trained H2O4GPU model and returns class predictions
 for classification and predicted values for regression.
 }
+\examples{
+\dontrun{
+
+library(h2o4gpu)
+
+# Setup dataset
+x <- iris[1:4]
+y <- as.integer(iris$Species) - 1
+
+# Initialize and train the classifier
+model <- h2o4gpu.random_forest_classifier() \%>\% fit(x, y)
+
+# Make predictions
+predictions <- model \%>\% predict(x)
+
+}
+
+}
diff --git a/src/interface_r/man/transform.h2o4gpu_model.Rd b/src/interface_r/man/transform.h2o4gpu_model.Rd
index 720df7809..8c60c1522 100644
--- a/src/interface_r/man/transform.h2o4gpu_model.Rd
+++ b/src/interface_r/man/transform.h2o4gpu_model.Rd
@@ -17,3 +17,25 @@ be used in generating predictions.}
 \description{
 This function transforms the given new data using a trained H2O4GPU model.
 }
+\examples{
+\dontrun{
+
+library(h2o4gpu)
+
+# Prepare data
+iris$Species <- as.integer(iris$Species) # convert to numeric data
+
+# Randomly sample 80\% of the rows for the training set
+set.seed(1)
+train_idx <- sample(1:nrow(iris), 0.8*nrow(iris)) 
+train <- iris[train_idx, ]
+test <- iris[-train_idx, ]
+
+# Train a K-Means model
+model_km <- h2o4gpu.kmeans(n_clusters = 3L) \%>\% fit(train)
+
+# Transform test data
+test_dist <- model_km \%>\% transform(test)
+
+}
+}