Ground movements cause significant damages to buildings. Currently, the common way of simulating structures to determine their various damage-type performances has proven to be inefficient especially when many structures have to be assessed. Hence, this study aims to develop an assessment model that could pre-dict the seismic capacity of 2–3 storey reinforced concrete (RC) residential build-ings in Davao City using Artificial Neural Network (ANN). The development of the said model consists primarily of factor identification, extraction of final factors, building simulation, modeling, and assessment. Thirteen potential factors af-fecting seismic capacity were identified, which were subjected to Exploratory Factor Analysis (EFA). The outcome of the EFA resulted in four major factors. Fourty-nine Ground Motion Data (GMD) and thirty-seven building samples were used and simulated using the SAP2000 program. This simulation was carried out by calculating the structural threshold of moderate damage with a 10% probability of exceedance using fragility curves and time-history analysis. The necessary data were collected, using them in the ANN modeling that yielded two Seismic Capacity System (SeiCapS) models for both the x- and y-directions. Thus, the predictive capacity was evaluated using R values of 0.7681 and 0.7036, respectively, which manifested the models’ capability to potentially predict seismic capacity.