The study of the fluid flow in heat exchangers and its related mass and heat transfer processes is a very complicated research subject. Modeling of coupled fluid flow and heat transfer processes using numerical and analytical approaches are cumbersome problems. Hence, the current study investigates the modeling thermo-hydraulic behavior of a helical plate heat exchanger (HPHE) using advanced machine learning models. The outlet temperatures of hot and cold fluids are predicted considering different cross-sectional areas and pitches of the flow channels. This paper fulfills the research gap about fluid flow and heat transfer mechanisms by considering advanced machine learning approaches. The proposed model helps different manufacturers of heat exchangers to estimate the thermal performance and characteristics. The developed model aims to provide an advanced random vector functional link (RVFL) optimized fire hawk optimizer to predict outlet temperatures of working fluids of HPHE. The proposed model was compared with four optimized models using grey wolf, jellyfish, sine-cosine, and hybrid salp swarm-whale optimizers. The results of all models were compared and fire hawk optimizers showed superior accuracy compared with other models. Fire hawk optimizer had the highest R2 (0.999) followed by jellyfish (0.998) for both investigated responses. Hybrid salp swarm-whale and sine-cosine optimizers had the lowest R2 (0.987) in the case of hot fluid.