Soiling of photovoltaic (PV) panels is a critical factor influencing solar energy yield, particularly in tropical regions where dust, humidity, rainfall, and biological contaminants accelerate performance degradation. This study investigates the impact of dust accumulation on PV panels through controlled experiments conducted over daily, weekly, and monthly intervals in Perak, Malaysia. Two panels—one cleaned regularly and one left exposed—were monitored to measure variations in output power and quantify accumulated dust mass. The results show a clear correlation between exposure duration, dust accumulation, and performance loss, with dust masses recorded over one-day, one-week, and one-month intervals, respectively. These values correspond to measurable reductions in output power. The findings underscore the importance of environmental and operational factors in determining PV efficiency and highlight the economic implications of unmanaged soiling. Furthermore, the collected data serve as a foundation for integrating machine learning (ML) into predictive maintenance frameworks. By combining soiling profiles with environmental variables, ML models can forecast cleaning schedules, optimize maintenance actions, and reduce unnecessary costs while sustaining long-term energy output. This study demonstrates the potential of data-driven predictive maintenance as a robust strategy to enhance the reliability and economic viability of PV systems in soiling-prone regions.