Homelessness is a complex and persistent societal issue, often exacerbated by economic instability, housing shortages, and systemic inequities. Existing strategies primarily rely on reactive interventions, which, while essential, fail to provide proactive solutions for prevention. This study presents a novel machine learning-based framework for early homelessness prediction, integrating key socioeconomic, housing, and public health indicators. Utilizing a realworld dataset, we compare the predictive performance of two machine learning models— Random Forest and XGBoost—to assess their effectiveness in identifying high-risk populations. The results demonstrate that the Random Forest model consistently outperforms XGBoost, achieving a lower Mean Absolute Error (MAE) of 12.46, a lower Mean Squared Error (MSE) of 44,534.73, and a higher R² score of 0.996, indicating a superior fit. Feature importance analysis reveals that total homeless counts (pit_tot_hless_pit_hud) and individual homelessness rates are the most critical predictive factors, while economic conditions and housing market pressures also play significant roles. Furthermore, residuals analysis and error distribution comparisons illustrate that the Random Forest model maintains a more stable and consistent predictive capability across different demographic and geographic groups. Our research stands apart by integrating a high-dimensional, multi-source dataset to enhance predictive accuracy while addressin...