Abstract In this research, an ash accumulation prediction model based on the Improved Bald Eagle Algorithm (IBES) optimization is proposed, and a least-squares support vector machine (LSSVM) is used to investigate the correlation between climatic parameters and the quantity of ash deposition on PV panel surfaces. Ash deposit affects PV panel output power over time and is influenced by meteorological factors. Following a thorough selection procedure, meteorological variables like wind speed and rainfall are used in the model to forecast the amount of ash that will accumulate on the slab. The original condor method was prone to local optimum convergence and had a slow convergence rate. These weaknesses are addressed by the optimal selection of the next iteration, which introduces the Cauchy variation operator to mutate the population’s optimal individuals. The optimization parameters of the revised algorithm are incorporated into the model and results are compared to models of other optimization algorithms. The outcomes demonstrate that the IBES-LSSVM ash accumulation prediction model has a higher degree of fit and a reduced prediction error.