Nanofluids, which are produced by the dispersion of nanoparticles such as Al2O3 or carbon nanotubes in conventional coolants (water, oil etc.), possess good poten tial for enhancing the thermal performance of the nuclear reactor cooling system. It’s desirable in nuclear heat exchanger to improve heat extraction performace and maintain temperatures within safe operational limits. In this research, we aim to maximize fuel rod heat extraction while trying to accurately estimate and minimize energy losses due to viscous dissipation. We employ a novel numerical approach to solve the governing equations of nanofluid flow. The approach is based on an error detection–correction technique, whereby the numerical errors are first detected and then corrected in a systematic manner to generate improved solutions. This method has demonstrated strong potential in boundary layer flow problems and is applied in the present study to enhance velocity and temperature distribution prediction accu racy in nanofluid-based reactor. In addition, COMSOL Multiphysics simulations are performed to find the thermal–hydraulic characteristics of nanofluids as a function of varied flow parameters such as Reynolds number, Prandtl number, nanoparticle volume fraction etc. The datasets are used to train machine learning model that output predictive values of the optimal operating conditions for achieving the max imization of heat transfer. This integrated framework, combining novel numerical method, physics-based simulations, and machine learning, serves to more accurately understand viscous dissipation effects in nanofluid systems and provides a pathway toward developing next-generation nuclear cooling technologies with improved safety margins and utilizing energy and resources more efficiently