Abstract
The integration of the fourth industrial revolution technologies, including artificial intelligence (AI), machine learning (ML), the Internet of Things (IOT), digital twins, and blockchain, is advancing calorimetry and heat transfer in renewable energy systems. This review examines how these technologies improve thermal efficiency, enable real-time system monitoring, and support predictive maintenance across solar, wind, geothermal, and bioenergy applications. AI-driven models are discussed for optimizing complex heat transfer behaviors, while IoT frameworks facilitate continuous calorimetric data acquisition. Digital twins support virtual simulations, and blockchain ensures data security. A comprehensive evaluation of recent research identifies key challenges such as computational demands, data security, and policy gaps. The article proposes future directions such as developing hybrid AI-physics models, enhancing explainable AI, conducting long-term performance validation, and standardization frameworks to enable the reliable deployment of smart thermal management systems for renewable energy.