Industrial sectors produce a lot of waste heat in the continuous manufacturing and thermal processing processes, resulting in high energy loss, poor thermal process stability, low manufacturing efficiency and high carbon emission. Most of the existing thermal management and waste heat recovery techniques suffer from various drawbacks including inefficient heat redistribution, inadequate temperature stabilization, slower response mechanisms and low intelligent decision making capacity in a dynamic industrial scenario. To overcome these problems, this research propose the Hybrid Thermal Optimization Intelligence Framework (HYTHERM-OPT) which is an intelligent thermal management system integrating AI based heat prediction, fuzzy adaptive control, intelligent heat routing and optimized waste heat recovery mechanism for optimizing energy use in industries. The proposed framework continuously monitors the thermal situation of industrial processes, forecasts the generation of heat, controls temperature changes and redistributes the heat of the processes in a usable way between the different process units. The experimental assessments conducted in a Python environment prove the high thermal efficiencies of HYTHERM-OPT (87%) and the high temperature stabilization accuracy (98%). HYTHERM-OPT improve energy utilization and reduce carbon emissions in industrial processes. The results obtained are found to be reliable, energy-efficient and sustainable for next generation smart manufacturing systems in terms of industrial thermal management.