In this study, total heat transfer rate and pressure drop along a shell and tube heat exchanger (STHX) with 6 porous baffles are numerically investigated. To study the impacts of segmental porous baffles, three values for the permeability (10(-9) m(2), 10(-12) m(2), and 10(-15) m(2)), porosity (0.2, 0.5, and 0.8), and baffle cut (25%, 35%, 50%) were considered, and the output parameters were calculated. Low baffle cuts provided the highest heat transfer; however, it generated a considerable amount of pressure drop as well. Although the porosity of 0.2 was superior in terms of heat transfer, higher pressure drop at lower baffle cut is the obstacle to consider it as the optimum value. The data was then utilized to train an Artificial Neural Network (ANN) to characterize the STHX and perform the sensitivity analysis. Baffle cut had the highest impact on the heat transfer as well as pressure drop while the porosity had the least in both by having 5% contribution. Finally, using genetic algorithm (GA), the optimum values for permeability, porosity, and baffle cut are calculated to gain the maximum heat transfer and minimum pressure drop in the system.