Small and medium-sized enterprises (SMEs) are the backbone of Mexico’s economy; however, they frequently face high failure rates due to suboptimal location choices and limited access to market information. In this study, we introduce a geospatial framework for predicting the longevity of entrepreneurial SMEs in Mexico City, using veterinary clinics as a representative case. Our approach integrates multiple datasets—including socioeconomic, geographical, and criminal activity records—to construct an extensive feature set capturing neighborhood demographics, commercial surroundings, and urban infrastructure. We then apply supervised learning methods, including multilayer perceptrons, random forests, and gradient boosting classifiers, to classify the expected lifespan of each business establishment into three survival groups (1–2, 3–4, and 5–6 years). Experiments with cross-validation reveal that an optimized XGBoost model achieves a balanced accuracy of 88.68% on a held-out test set, outperforming other evaluated methods. A cluster analysis of nearby business types further identifies pivotal commercial co-locations—such as grocery stores, dining establishments, and complementary retail—that foster longer SME survival. This finding underscores the importance of geographic context, socioeconomic status, and consumer traffic patterns in determining SME viability. While our study focuses on veterinary clinics, the proposed methodology generalizes to other commercial sectors and can aid policymakers and entrepreneurs in mitigating failure risks through data-driven site selection. Overall, this work provides a robust framework for combining geospatial analysis and machine learning to enhance the strategic decision-making process for SMEs in metropolitan settings.