Thermal interface materials (TIMs), including high-performance thermal greases and high-conductivity viscous compounds, are critical enablers of heat dissipation in modern high-power electronics. In field deployments and repair scenarios, TIM degradation, voiding, or incomplete coverage can cause elevated junction temperatures, excessive fan operation, performance throttling, and accelerated component aging. This white paper proposes a practical approach for optical verification of TIM application using ultraviolet (UV) fluorescence, combined with AI-ready data collection that enables computer vision methods to learn visual patterns associated with adequate or inadequate thermal contact. The approach is aligned with the broader scope of an AI-enabled remote computer diagnostic and data-recovery research project. The paper contributes a multimodal dataset specification and an empirical case study demonstrating that thermal interface optimization materially affects GPU/VRAM temperatures, fan behavior, and benchmark performance under comparable load and board power. This research was carried out as part of the project Data Recovery and Remote Computer Diagnostic Services with AI (Project code: ΕΚΠΑΡ01-0063510) under the framework of the Action Research – Innovate of the Operational Program Competitiveness 2021-2027, that is co-funded by the European Regional Development Fund (ERDF) and Greece.