In the field of oil exploration and development, accurate prediction of mechanical drilling rate is crucial for improving drilling efficiency and reducing engineering risks. Accurate mechanical drilling rate prediction provides an important basis for formulating drilling plans and assessing drilling risks. However, traditional drilling rate equations and machine learning methods cannot fully consider the factors affecting the mechanical drilling rate in complex nonlinear drilling systems. This paper presents a mechanical drilling rate prediction model based on a genetic algorithm optimized backpropagation neural network (GA-BPNN), using historical drilling data from an oil field in the South China Sea. The data preprocessing includes SG smoothing, normalization, and comprehensive feature parameter selection through Pearson, Spearman, and Kendall correlation coefficients. The model is compared and verified with BP, RBF, MEA-BP neural network models, and traditional machine learning methods such as ELM, RF, SVM, and KNN. The experimental results show that the GA-BP has an R2 of 0.967, and the predicted values are in good agreement with the measured values, with an accuracy increase of 17.64% compared to the standard BP neural network prediction R2, and more accurate predictions than other models. This hybrid intelligent prediction model can accurately predict and prevent drilling accidents, provide effective data for guiding oil field drilling construction parameters, thereby improving the economic benefits of drilling construction.