This research focuses on the need to clearly define the lateral design force distribution shape for the seismic design of reinforced concrete (RC) buildings. It specifically looks at the effects of soil-structure interaction (SSI) and inelastic structural behavior. Traditional seismic design methods usually assume fixed-base conditions and overlook SSI. This can result in inaccurate predictions of how much the structure will deform. To create a more reliable, data-driven seismic design framework, the study conducted extensive nonlinear time-history analyses on various regular RC buildings using OpenSeesPy both for generating a building database based on current code provisions and for assessing their dynamic response. These models vary across important factors, including fundamental periods, slenderness ratios, , and soil conditions. The spectral acceleration at the first mode period served as the measure of ground motion intensity (IM). The collected data were used to derive classical regression equations and to train machine learning models, including Neural Network Regression and Gradient-Boosting Regression Trees, to predict the optimal lateral force profile shape. These methods are essential for understanding the complex, nonlinear relationships between seismic input factors and the dynamic characteristics of a building, as well as engineering demand parameters (EDPs) such as storey drift. The study found that the key factors affecting nonlinear lateral displacement and force-shape profile include the structure's slenderness ratio , the fixed-base and flexible-base fundamental periods, and , and the IM, . By providing a probabilistic assessment, this methodology seeks to improve the outcomes of seismic design codes and enhance performance-based design for RC buildings.