This study embarks on a comprehensive analysis of a regional Water Supply System (WSS), focusing on the development of predictive models to support the management and modernization of the system. Highlighting the need for advanced predictive models in the face of aging infrastructure and variable demand patterns, I assess the effectiveness of modernization efforts within a WSS through operational data analysis. Methods: Utilizing a case study approach, we applied statistical analyses to failure and recovery data, aiming to extract characteristic parameters crucial for predictive modeling. The data included failure frequencies, recovery times, and their impact on WSS performance. Results: The analysis yielded a global model predicting WSS availability and reliability, incorporating failure and recovery rates to evaluate the system's operational efficiency. The findings demonstrate the model's potential to predict WSS performance, guiding decision-making for future infrastructure investments and operational strategies. Conclusions: The predictive modeling approach offers significant insights into the WSS's management, indicating that such methodologies can enhance the reliability and efficiency of water supply systems. Future work should focus on refining these models with more extensive datasets, considering the variability of external factors like climate change and urban growth on system performance. The predictive modeling approach offers significant insights into the WSS's management, indicating that such methodologies can enhance the reliability and efficiency of water supply systems. Future work should focus on refining these models with more extensive datasets, considering the variability of external factors like climate change and urban growth on system performance. This underscores the importance of integrating predictive models into the strategic planning and operational frameworks of water supply systems to ensure sustainable water management in response to evolving environmental and societal demands.