diff --git a/ivy/functional/frontends/paddle/nn/functional/common.py b/ivy/functional/frontends/paddle/nn/functional/common.py
index 442b971447a97..4d7ee4f32d23e 100644
--- a/ivy/functional/frontends/paddle/nn/functional/common.py
+++ b/ivy/functional/frontends/paddle/nn/functional/common.py
@@ -23,3 +23,39 @@ def cosine_similarity(x1, x2, *, axis=1, eps=1e-08):
 
     cosine = numerator / denominator
     return cosine
+
+
+def get_mask(shape, device, prob, seed=None):
+    mask = ivy.where(
+        ivy.random_uniform(shape=shape, device=device, seed=seed) < prob,
+        0.0,
+        1.0,
+    )
+    return mask
+
+
+@with_supported_dtypes({"2.4.1 and below": ("float32", "float64")}, "paddle")
+@to_ivy_arrays_and_back
+def dropout(x, p=0.5, axis=None, training=True, mode="upscale_in_train", name=None):
+    if axis > 1:
+        raise ValueError("Axis value can only be 0 or 1 or None.")
+    elif axis is None or (isinstance(axis, list) and len(axis) == 2):
+        mask = get_mask(shape=x.shape, device=ivy.dev(x), prob=p, seed=None)
+    elif axis == 0:
+        mask = get_mask(shape=(x.shape[0], 1), device=ivy.dev(x), prob=p)
+        mask = ivy.broadcast_to(mask, x.shape)
+    elif axis == 1:
+        mask = get_mask(shape=(1, x.shape[1]), device=ivy.dev(x), prob=p)
+        mask = ivy.broadcast_to(mask, x.shape)
+    if mode == "upscale_in_train":
+        if training:
+            out = ivy.multiply(x, mask)
+            ret = ivy.multiply(out, 1.0 / (1.0 - p))
+        else:
+            ret = x
+    else:
+        if training:
+            ret = ivy.multiply(x, mask)
+        else:
+            ret = ivy.multiply(x, (1.0 - p))
+    return ret
diff --git a/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py b/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py
index f0e5be38decea..43f5e76f64b3b 100644
--- a/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py
+++ b/ivy_tests/test_ivy/test_frontends/test_paddle/test_nn/test_functional/test_paddle_common.py
@@ -41,3 +41,50 @@ def test_paddle_cosine_similarity(
         x2=x[1],
         axis=axis,
     )
+
+
+# dropout
+@handle_frontend_test(
+    fn_tree="paddle.nn.functional.common.dropout",
+    d_type_and_x=helpers.dtype_and_values(
+        available_dtypes=helpers.get_dtypes("float"),
+        num_arrays=1,
+        shared_dtype=True,
+        min_value=2,
+        max_value=5,
+        min_num_dims=1,
+        max_num_dims=1,
+        min_dim_size=2,
+    ),
+    p=st.floats(min_value=0.0, max_value=1.0),
+    axis=st.integers(min_value=0, max_value=1),
+    training=st.booleans(),
+    mode=st.one_of(
+        *[st.just(seq) for seq in ["upscale_in_train", "downscale_in_infer"]]
+    ),
+)
+def test_paddle_dropout(
+    *,
+    d_type_and_x,
+    p,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    training,
+    axis,
+    mode,
+):
+    dtype, x = d_type_and_x
+    helpers.test_frontend_function(
+        input_dtypes=dtype,
+        p=p,
+        frontend=frontend,
+        fn_tree=fn_tree,
+        test_flags=test_flags,
+        on_device=on_device,
+        x=x[0],
+        training=training,
+        axis=axis,
+        mode=mode,
+    )