Abstract— Evaluation of defects in pipeline installed in underwater demands smart engineered solutions. Burying the large pipelines in underwater facilitated for the purpose of petroleum, LPG, potable water and other gaseous requirements are increasing every decade. Underwater pipeline route tracking during leakage or any emergency conditions is necessary and is challenging. The detection of pipelines in underwater is made easy through SONAR imaging and is evaluated through state-of-the-art technology. So heuristic approach is formulated here using the emerging machine learning technique. The research framework is focused on design and analysis using efficient machine learning algorithm, which can detect buried pipelines. Various statistical parameters are selected here for the comparison of highly compete algorithms named convolutional neural networks (CNN), Fuzzy C means clustering and Apriori Algorithm. The system design is adopted using MATLAB IDE tool and simulated results are validated and tested. 
 
Keywords: Pipeline detection, apriori algorithm, convolutional neural network, Machine learning , segmentation