diff --git a/hls4ml/converters/pytorch/reshape.py b/hls4ml/converters/pytorch/reshape.py
index f7392ab8da..64b60c97b9 100644
--- a/hls4ml/converters/pytorch/reshape.py
+++ b/hls4ml/converters/pytorch/reshape.py
@@ -161,3 +161,86 @@ def handle_upsample(operation, layer_name, input_names, input_shapes, node, clas
     layer['align_corners'] = bool(class_object.align_corners)
 
     return layer, output_shape
+
+
+@pytorch_handler('ConstantPad2d')
+def parse_constantpad2d_layer(operation, layer_name, input_names, input_shapes, node, class_object, data_reader, config):
+    assert operation == 'ConstantPad2d'
+
+    layer = {}
+    layer['class_name'] = 'ZeroPadding2D'
+    layer['name'] = layer_name
+    layer['inputs'] = input_names
+
+    # PyTorch padding is (left, right, top, bottom)
+    padding = class_object.padding
+    if isinstance(padding, int):
+        pad_left = pad_right = pad_top = pad_bottom = padding
+    elif isinstance(padding, (tuple, list)) and len(padding) == 4:
+        pad_left, pad_right, pad_top, pad_bottom = padding
+    else:
+        raise Exception(f'Unsupported padding format: {padding}')
+
+    layer['pad_left'] = pad_left
+    layer['pad_right'] = pad_right
+    layer['pad_top'] = pad_top
+    layer['pad_bottom'] = pad_bottom
+
+    # Only support zero padding for now
+    pad_value = getattr(class_object, 'value', 0)
+    if pad_value != 0:
+        raise Exception('Only zero padding is supported for ConstantPad2d in hls4ml')
+
+    # Compute output shape
+    batch, channels, height, width = input_shapes[0]
+    out_height = height + pad_top + pad_bottom
+    out_width = width + pad_left + pad_right
+    output_shape = [batch, channels, out_height, out_width]
+
+    # Add required attributes for hls4ml
+    layer['n_chan'] = channels
+    layer['in_height'] = height
+    layer['in_width'] = width
+    layer['out_height'] = out_height
+    layer['out_width'] = out_width
+
+    return layer, output_shape
+
+
+@pytorch_handler('ConstantPad1d')
+def parse_constantpad1d_layer(operation, layer_name, input_names, input_shapes, node, class_object, data_reader, config):
+    assert operation == 'ConstantPad1d'
+
+    layer = {}
+    layer['class_name'] = 'ZeroPadding1D'
+    layer['name'] = layer_name
+    layer['inputs'] = input_names
+
+    # PyTorch padding is (left, right)
+    padding = class_object.padding
+    if isinstance(padding, int):
+        pad_left = pad_right = padding
+    elif isinstance(padding, (tuple, list)) and len(padding) == 2:
+        pad_left, pad_right = padding
+    else:
+        raise Exception(f'Unsupported padding format: {padding}')
+
+    layer['pad_left'] = pad_left
+    layer['pad_right'] = pad_right
+
+    # Only support zero padding for now
+    pad_value = getattr(class_object, 'value', 0)
+    if pad_value != 0:
+        raise Exception('Only zero padding is supported for ConstantPad1d in hls4ml')
+
+    # Compute output shape
+    batch, channels, width = input_shapes[0]
+    out_width = width + pad_left + pad_right
+    output_shape = [batch, channels, out_width]
+
+    # Add required attributes for hls4ml
+    layer['n_chan'] = channels
+    layer['in_width'] = width
+    layer['out_width'] = out_width
+
+    return layer, output_shape
diff --git a/test/pytest/test_pytorch_constpadmapping.py b/test/pytest/test_pytorch_constpadmapping.py
new file mode 100644
index 0000000000..b4f602d711
--- /dev/null
+++ b/test/pytest/test_pytorch_constpadmapping.py
@@ -0,0 +1,44 @@
+import torch.nn as nn
+
+from hls4ml.converters import convert_from_pytorch_model
+from hls4ml.utils.config import config_from_pytorch_model
+
+
+def test_pytorch_constantpad_1d_2d():
+    class Pad1DModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.pad = nn.ConstantPad1d((2, 3), 0)  # pad 2 left, 3 right
+
+        def forward(self, x):
+            return self.pad(x)
+
+    class Pad2DModel(nn.Module):
+        def __init__(self):
+            super().__init__()
+            self.pad = nn.ConstantPad2d((1, 2, 3, 4), 0)  # left, right, top, bottom
+
+        def forward(self, x):
+            return self.pad(x)
+
+    # 1D test: batch=1, channels=2, width=4, values 1,2,3,4
+    model_1d = Pad1DModel()
+    model_1d.eval()
+    config_1d = config_from_pytorch_model(model_1d, (2, 4))
+    hls_model_1d = convert_from_pytorch_model(model_1d, hls_config=config_1d)
+    print("1D Padding Model Layers:")
+    for layer in hls_model_1d.get_layers():
+        print(f"{layer.name}: {layer.class_name}")
+
+    # 2D test: batch=1, channels=1, height=2, width=4, values 1,2,3,4,5,6,7,8
+    model_2d = Pad2DModel()
+    model_2d.eval()
+    config_2d = config_from_pytorch_model(model_2d, (1, 2, 4))
+    hls_model_2d = convert_from_pytorch_model(model_2d, hls_config=config_2d)
+    print("2D Padding Model Layers:")
+    for layer in hls_model_2d.get_layers():
+        print(f"{layer.name}: {layer.class_name}")
+
+    # Write the HLS projects, cannot compile on Windows
+    hls_model_1d.write()
+    hls_model_2d.write()