The optimization of energy consumption and heat recovery in industrial processes is a priority challenge in modern engineering, particularly in systems that employ mixing tanks for thermal operations. These units, widely used in sectors such as agro-industry, food engineering, water treatment, and the chemical industry, require precise control of temperature, mixing homogeneity, and the utilization of residual heat. Artificial intelligence (AI), particularly predictive models based on machine learning, offers advanced tools to model and optimize the thermodynamic behaviour of these systems under variable operating conditions.This study proposes the development and implementation of predictive models trained with both experimental data and numerical simulations to estimate in real time the key parameters that determine the energy efficiency of mixing tanks. The approach integrates variables such as inlet and outlet temperatures, mixing flow rate, fluid properties (viscosity, density, specific heat capacity), supplied power, thermal losses, and heat recovery through associated exchangers. The AI architecture combines deep neural networks for nonlinear variable estimation with optimization algorithms based on metaheuristics (such as Particle Swarm Optimization or Genetic Algorithms) for dynamic adjustment of operating parameters. The proposed model not only predicts thermal behaviour but also recommends operating strategies to maximize residual heat recovery, thereby reducing primary energy demand. For validation, data obtained from experimental prototypes and Computational Fluid Dynamics (CFD) simulations—considering mixing phenomena, heat transfer, and temperature gradients—are employed. The accuracy of the predictions is evaluated using metrics such as Root Mean Square Error (RMSE) and the coefficient of determination (R²), achieving over 95% accuracy in test scenarios. The practical implementation of the model is integrated into a real-time monitoring and control platform, enabling operators to automatically adjust agitation conditions, flow rates, and target temperatures. This approach directly contributes to reducing energy consumption and improving the sustainability of industrial processes, aligning with circular economy strategies and carbon footprint reduction goals. Preliminary results demonstrate that combining AI with advanced predictive modelling in mixing tanks increases thermal efficiency.