https://matjournals.net/engineering/index.php/JMTMS <p><strong>JMTMS</strong> is a peer reviewed Journal in the discipline of Engineering published by the MAT Journals Pvt. Ltd. The Journal provides a platform to Researchers, Academicians, Scholars, Professionals and students in the Domain of Mechanical Engineering to promulgate their Research/Review/Case studies in the field of Thermodynamics in Mechanical System. The Journal aims to promote high quality empirical Research, Review articles, case studies and short communications mainly focused on Mechanics of materials and structures, chemical systems, Heat Engine, Thermo economics, statistical, chemical, Atmospheric, Biological Thermodynamics, Equilibrium and Non Equilibriums, Origin of Heat Energy on Earth, Energy Control Process, and Metal forming.</p> en-US Journal of Modern Thermodynamics in Mechanical System https://matjournals.net/engineering/index.php/JMTMS/article/view/4060 <p><em>Phase Change Materials (PCMs) are central to thermal energy storage systems, but selecting the most suitable material remains difficult because thermal performance depends on many interacting properties. Machine learning (ML) offers a practical route for predictive selection by learning relationships among composition, structure, thermophysical properties, and performance indicators. This review summarizes current research on ML-based prediction and optimization for PCM selection, with emphasis on melting temperature, latent heat, thermal conductivity, density, stability, and cycling reliability. Recent studies show that regression, ensemble learning, artificial neural networks, and hybrid AI workflows can reduce experimental effort while improving screening speed and model accuracy. The literature also indicates that data quality, feature engineering, cross-study comparability of reported accuracy metrics, and interpretability remain major challenges for adoption in materials design; this review cross-checks representative quantitative claims against their original sources and reports the resulting corrections explicitly. Based on the reviewed evidence, ML is not replacing experiments; rather, it is complementing them by narrowing the candidate pool and guiding targeted validation. The paper concludes that future PCM discovery will likely depend on integrated datasets, explainable models, and domain-aware optimization frameworks that link material chemistry with storage performance.</em></p> F. K. Pathan A. I. Gond S. D. Aade P. D. Rathod G. N. Deshpande Copyright (c) 2026 Journal of Modern Thermodynamics in Mechanical System 2026-09-01 2026-09-01 1 13