Climate change presents an urgent global challenge, necessitating innovative and data-driven solutions for carbon mitigation. Universities, as hubs of research and innovation, are uniquely positioned to lead the implementation of sustainable energy transitions. This study develops a comprehensive methodological framework to evaluate and optimize rooftop solar energy potential across Sakarya University, using high-resolution Unmanned Aerial Vehicle (UAV) data integrated with Multi-Criteria Decision-Making (MCDM) techniques. A total of 70 buildings were identified as suitable for photovoltaic (PV) installation, yielding a usable rooftop area of approximately 44,500 m² after filtering out structural constraints and shading elements. Rooftop Global Horizontal Irradiance (GHI) ranged from 875 to 1,300 kWh/m² annually. The estimated cumulative solar potential across all rooftops reached ~53,000 MWh/year, with individual building potential varying between 21 and 2,300 MWh/year. Educational and administrative buildings emerged as the primary contributors, together accounting for more than 70% of the total solar yield, with educational facilities alone offering ~27,000 MWh/year. To prioritize rooftop installations, two criteria weighting techniques—Ordinal Priority Approach (OPA) and Ranking Comparison (RANCOM)—were applied, followed by the implementation of five recent and efficient MCDM methods: RAM, PROBID, MARCOS, SPOTIS, and EDAS. The final ranking of alternatives was synthesized using the Footrule Aggregation method. All computations were carried out in the Google Colab environment utilizing the Python libraries PyMCDM, pyDecision, and pyRankMCDA. With the deployment of around 16,500 solar panels (500 W, 24% efficiency), the university could install an 8.2 MW PV system, capable of generating approximately 22.18 GWh annually—more than double the current campus electricity demand of 9.7 GWh. This surplus of 12.48 GWh creates opportunities for future energy expansion and integration with electric mobility systems. Financial analysis revealed a Net Present Cost (NPC) of ~36,000 TRY/kW (~900–950 USD/kW), confirming the economic viability of rooftop PV infrastructure, especially given the remarkably low Levelized Cost of Energy (LCOE). The integration of UAV-derived geospatial data, open-source modeling tools, and MCDM-based decision frameworks offers a scalable, transferable model for data-informed policy-making in higher education institutions. This study aligns with key Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 11 (Sustainable Cities and Communities), and positions universities as strategic actors in advancing climate action and energy resilience. These findings contribute practical insights into the operationalization of campus-scale decarbonization strategies, offering a replicable roadmap for sustainability leadership in the academic sector.