Technovigilance in Mexico is regulated by NOM-240-SSA1-2012 standard; however, its practical implementation in hospital settings remains limited and underreported. Across Latin America, systemic challenges, such as fragmented operational structures, low reporting culture, and limited human resources, have hindered the transition from passive to proactive surveillance of medical devices. This paper introduces PRÆVIDA, a hospital-based technovigilance framework designed to enable predictive risk management through the integration of structured incident workflows, systematic digital data capture, and a machine learning-driven classification module. Developed under a low-code infrastructure, the system captures pre-event variables and generates real-time risk alerts, triggering predefined response protocols to mitigate potential harm before adverse events occur. The model was trained using a hybrid dataset composed of anonymized real-world records and simulated cases created from observed patterns in operational hospital environments. Preliminary testing on 160 labeled cases yielded strong classification metrics, including 96.9% accuracy and 0.997 AUC. While these results are promising, they were obtained using the training dataset and may overestimate real-world performance. Internal validation by the hospital’s technovigilance team has supported the model’s clinical relevance and usability. PRÆVIDA constitutes a scalable, data-informed solution for strengthening medical device safety oversight in resource-limited settings. By aligning with WHO guidelines and the Mexican regulatory framework, it offers a replicable pathway toward modernizing technovigilance systems in low and middle income countries.