This research pioneers the application of a diverse set of advanced machine learning and optimization methods, to predict the erodibility of lateritic soil treated with cement and nanostructured quarry fines, providing a groundbreaking, data-driven approach that enhances traditional erosion analysis techniques. Traditional experimental methods for erosion analysis are often complex and resource-intensive; therefore, this research focuses on developing predictive models using Python. To build the machine learning and optimization models, 121 data points were collected from existing literature. The dataset includes erodibility measurements of unsaturated lateritic soil treated with local cement and enhanced with nanostructured quarry fines. The study employs Artificial Neural Networks (ANN), Random Forest (RF), Support Vector Machine (SVM), XGBoost, CatBoost, and Particle Swarm Optimization (PSO) to predict soil erodibility. The data was divided into training (70%), testing (15%), and validation (15%) sets for model development and evaluation. Model performance was assessed using statistical metrics such as $R^2$, M.A.E., M.S.E., R.M.S.E., and M.A.P.E. The results indicated an $R^2$ value are almost equal to 1 in training, testing, and validation phases, and the M.A.P.E. values are below 3% for the CatBoost, RF, XGB, SVM, and ANN models across all three phases: training, testing, and validation. The CatBoost, RF, XGB, SVM, and ANN models are most accurate in predicting the erodibility. Finally, relative importance showed that maximum unit weight and hydrated cement are the most influencing parameter in predicting the erodibility.