This study is aimed at developing prediction model for structural behavior of Porous Asphalt Pavement (PAP) using ABAQUS software and also developing ANN (Artificial Neural Network) model to predict void percentages in Porous Asphalt mix gradations. Data from the past literatures were used to analyze various mixing parameters affecting the properties of Porous Asphalt gradation mixes. The study analyzed PAPs using KENPAVE software. The findings indicated that fewer allowable repetitions to fatigue and rutting failures were observed for thinner Porous Asphalt Concrete (PAC) layer thicknesses. This suggests that thicker PAC layers may offer better resistance to fatigue and rutting failures in pavement systems. The study found that the nature of subgrade material significantly influenced the rutting performance of PAP systems. Specifically, clayey soils exhibited a 77.74% reduction in the design life of the pavement compared to gravelly soil subgrades. This highlights the importance of considering subgrade characteristics in pavement design and construction to optimize pavement performance and longevity. Higher contact pressures resulted in higher tensile stresses at the bottom of PAC layer which in turn reduced the fatigue life of PAP. The findings from ABAQUS analysis indicated that an increase in the void percentage in Porous Asphalt Concrete (PAC) mixes led to a significant increase in horizontal tensile strain at the bottom of the PAC layer, with a 12.3% increase observed. Additionally, there was a noticeable increase in tensile strain for a PAC mix with 28% voids compared to a mix with 16% voids. This suggests that higher void percentages in the PAC mixes can potentially enhance the flexibility and deformation resistance of the pavement structure, which may contribute to improved performance under various loading conditions, hence leading to 92.8% reduction in allowable load repetitions to fatigue failure.The study further revealed that an increased void percentage in Porous Asphalt Concrete (PAC) mixes resulted in a decrease in vertical stress and deflection in the PAP system. However, no significant effect on the allowable repetitions to rutting failure was observed. This suggests that higher void percentages may lead to better load distribution and reduced vertical stresses within the pavement structure, contributing to improved performance in terms of stress and deflection.Moreover, Asphalt Pavement mixes were compiled to develop an Artificial Neural Network (ANN) prediction model. The results demonstrated good conformity between predicted and actual data, with a mean square error of 0.109 and a coefficient of correlation of 0.994. This indicates that the ANN model accurately predicts pavement performance based on the compiled asphalt pavement mixes, providing a valuable tool for pavement design and analysis.