Traditional drilling speed prediction models have often been constrained by issues such as high data dimensionality and feature correlation, resulting in limited efficiency and accuracy of drilling speed prediction. To address these issues, a drilling speed prediction algorithm model based on Particle Swarm Optimization (PSO) with integrated feature selection has been proposed in this paper. Based on data preprocessing, three key parameters, threshold_1, threshold_2, and threshold_3, have been chosen as optimization targets, and a fitness function has been constructed by combining historical data and the Particle Swarm Optimization algorithm, thereby establishing the drilling speed prediction model. Subsequently, the proposed drilling speed prediction method has been validated using actual drilling data and compared with traditional machine learning algorithm models. Experimental results have shown that the proposed Particle Swarm Optimization-based integrated feature selection algorithm has achieved higher efficiency and accuracy in feature selection. The accuracy of the four machine learning drilling speed prediction models trained using the optimized integrated feature selection results has been improved by 59%, 1%, 3%, and 1%, respectively, compared to before optimization, and by 24%, 2%, 4%, and 3%, respectively, compared to models trained using all features. This paper has provided an effective feature selection method for cases where too many feature parameters have been extracted in drilling engineering, and it offers significant guidance for the practical application of feature selection algorithms in the engineering field.