As universities increasingly embrace sustainability frameworks in response to global climate challenges, optimizing energy and water use within campus infrastructure has emerged as both a strategic priority and a practical opportunity. This thesis presents a comprehensive case study of the Universitat Politècnica de Catalunya (UPC), with a focus on evaluating rooftop photovoltaic (PV) generation, electricity and water consumption, and building-level sustainability performance across the campus. Using the FME building as an example, the study first explores the temporal dynamics of rooftop PV generation throughout 2024. Hourly energy production data was analyzed to assess alignment between solar availability and building demand, and self-consumption ratios were derived to evaluate system utilization efficiency. Clustering methods were employed to classify daily PV generation profiles into distinct solar pattern types. In parallel, the study performs a detailed analysis of electricity and water usage across multiple academic buildings. All consumption values were normalized by usable floor area to allow for cross-building comparison on an equal basis. Daily-level data analysis revealed variations in usage behavior, while anomaly detection techniques were used to identify periods of abnormal or inefficient resource use. Benchmarking against recommended sustainability standards provided further context for evaluating performance. To integrate these diverse resource metrics into a unified evaluation tool, a composite sustainability index was developed. This index aggregates four normalized indicators—electricity consumption, water consumption, PV contribution, and CO2 emissions—enabling a comparative ranking of buildings based on environmental performance. The methodology proposed in this thesis demonstrates the power of data-driven analysis for campus sustainability assessment. It offers a replicable and scalable approach that can be adapted to other academic institutions seeking to monitor, evaluate, and improve their environmental footprint. By combining technical analysis with actionable metrics, the research provides valuable insight for universities aiming to transition toward more efficient, low-carbon operations through the intelligent management of local renewable energy and resource consumption.