Compact heat exchangers are an essential device for efficiently transferring thermal energy between fluids. In contrast to larger objects of the same kind, they do this by optimizing the surface area for heat transmission inside a more compact space. This study theoretically calculates the heat transfer coefficient and pressure drop of a plate compact heat exchanger using design parameters such as air flow rate (1-4 kg/s), inlet air temperature (300-350 K), outlet air temperature (400-450 K), passage width (0.003-0.05 m), divider width (0.001-0.005 m), and heat exchanger length (0.15-1 m) at atmospheric pressure (101325 Pa). The study uses the Levenberg-Marquardt artificial neural network method to investigate heat transfer coefficients in compact heat exchangers. Results demonstration that the coefficient increases with mass flow rate doubling and 93% tripling. The maximum coefficient increases by 7.9% with a divider width of 0.001 m, while it decreases with shorter route lengths. The investigation revealed that pressure decreases exhibit a more pronounced rise in relation to the width of the divider. The factors increased by 1.04, 1.08, 1.13, 1.17, and 1.22 for divider widths of 0.001 m and 0.005 m, respectively, at varying temperatures. The change in the width of the divider had little effect on the pressure decrease at constant average temperatures. The Mean Squared Error (MSE) for heat transfer was -0.1787, whereas the MSE for pressure drop was 3.988. The training phase of the ANN was flawless, achieving projected values over 200 W/m2K and a pressure decrease surpassing 5000 Pa.