As the popularity of electric vehicles and the development of wireless charging technology have prompted a focus on charging efficiency, determining the optimal charging location has become an important issue to improve charging efficiency and user experience. The aim of this study is to develop a method for predicting the efficiency prediction of wireless charging locations for electric vehicles using Black Kite Algorithms (BKA) and Support Vector Regression (SVR). First, we screened the factors affecting charging efficiency by Spearman’s correlation coefficient to identify the most relevant factors. Then, this paper compares four common prediction methods, including BP, RBF, RF and SVR, and identifies SVR as the benchmark model due to its better performance in charging efficiency prediction. Subsequently, this paper further optimizes the SVR model, examines the effects of two optimization algorithms, BKA and PSO, and finally selects BKA as the optimal algorithm. The effectiveness and practicality of the proposed method are verified through simulation and experiment, and the results show that it has a high prediction accuracy and provides new ideas and methods for the design and optimization of wireless charging systems for electric vehicles.