4b038382-a69a-4e1f-8f9a-8d0c83f6c03c20250624014231584wseas:wseasmdt@crossref.orgMDT DepositWSEAS TRANSACTIONS ON ENVIRONMENT AND DEVELOPMENT2224-34961790-507910.37394/232015http://wseas.org/wseas/cms.action?id=4031110202511020252110.37394/232015.2025.21https://wseas.com/journals/ead/2025.phpBlessy JayaronJoseDepartment of Mathematics and Statistics, Banasthali Vidyapith, Banasthali, Rajasthan, INDIAPreetiJainDepartment of Mathematics and Statistics, Banasthali Vidyapith, Banasthali, Rajasthan, INDIAT. RajaRaniFoundation Programme Department, Military Technological College, OMANThis study investigates energy losses in crude oil pipelines to optimize design, improve efficiency, and enhance safety. Pipelines made of AISI1020 steel were modeled as three equal-length sections with varying diameters to replicate real-world conditions. COMSOL Multiphysics simulations were conducted to analyze pipeline behavior under different heat and flow scenarios. Temperature-related challenges were a primary focus due to their impact on energy dissipation. A quantile loss prediction approach identified the best-performing models. Based on machine learning model metrics and quantile loss, the best prediction models were analyzed for each output. For instance, for the average Head Loss (HL_Avg), the Random forest-tuned model emerged as the best and most balanced model, excelling across all metrics and quantiles while offering high accuracy and minimizing overfitting risks. Further, the analysis of SHAP values to assess the influence of key parameters such as fluid velocity, temperature gradients, and pipeline geometry is a novel approach that enhances the interpretability of model predictions. 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