Additive manufacturing, particularly 3D printing, often produces surface ridges, especially for complex geometries, that require post-processing to achieve a smooth finish. Laser ablation is an effective technique for smoothing these surfaces, but precise identification of ridges is crucial for optimizing the process. This study explores the use of machine learning to detect and ablate 3D print ridges, improving the accuracy of laser smoothing. A convolutional neural network (CNN) was trained on greyscale images of printed surfaces, learning to segment ridge regions from background material. From there, image processing filters and a line transform was applied to gather line defining information to be converted into DXF, a readable file for the laser software. The trained model was integrated into a graphical user interface (GUI) to automate ridge detection and guide the laser for targeted ablation, minimizing manual intervention. The system was validated on test parts, demonstrating overall efficiency and accuracy in ridge identification. Other experiments were done to determine proper laser and process parameters to achieve an accurate and smooth surface finish. The experimental results showed improved surface uniformity. The automated approach made laser smoothing efficient and scalable for industrial and manufacturing applications. By leveraging machine learning, this method advances the precision and repeatability of post-processing in 3D printing, reducing labor costs and improving final product quality.