This study analyzes horizontal earthquake loads in shear-wall-free reinforced concrete buildings using data mining and machine learning. A synthetic dataset was created based on Turkey’s 2018 Earthquake Code. Various preprocessing and feature selection techniques were applied. The findings show that accurate load predictions can be made even without using all regulatory parameters—particularly SS and HN—by leveraging neural networks, random forests, and SVMs. This research contributes to the field by questioning standard assumptions and offering a data-driven alternative for structural earthquake assessments.