Pipelines serve as a backbone for a wide on-ground and underground industrial infrastructure, such as oil, gas, water, and sewage. They protect the environment by localizing and transporting their contents in safe housing. However, a significant challenge is faced in case of leakage due to pipeline defects, which may arise due to multiple factors, ranging from manufacturing processes in the factory to on-site operations. The presence of these defects not only carries environmental risks and associated maintenance costs but can also result in substantial disruptions and financial loss within pipeline transportation operations. Thus, detecting defects at every stage of production and usage is crucial. Currently, various methods are employed to identify these defects. However, the integration of visual machine learning techniques, particularly in conjunction with deep learning methodologies, has opened up new avenues for pipeline monitoring and troubleshooting. This chapter details a comprehensive comparison of deep learning-based methods, provides a succinct overview of conventional techniques, and evaluates the performance of these methods in terms of accuracy, applicability, and their ability to detect the sizes and types of defects in pipelines. Furthermore, it explores the challenges and potential prospects of implementing these algorithms, providing valuable insights for researchers in the field.