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The study presents an innovative hybrid machine learning model tailored for predicting the load-displacement characteristics of bored piles, specifically those integral to high-rise buildings. Incorporating critical design parameters—diameter, length, Standard Penetration Test (SPT) indices, and effective overburden pressure—the model leverages a dataset of 1650 samples from static load tests in Vietnam. This hybrid approach integrates the Three Modified Symbiotic Organisms Search algorithm (3mSOS) with the Levenberg–Marquardt backpropagation neural network (LMNN) to establish the intricate relationship between these design parameters and the load-displacement response of the piles. Numerical results underscore the model's exceptional performance in accurately predicting the load-displacement behavior of bored piles. Rigorous validation employs an independent dataset derived from bidirectional pile load tests, affirming the model's reliability. A comprehensive sensitivity analysis provides valuable insights into the mechanisms governing load-bearing. Feature importance analysis and partial dependence plots reveal nuanced relationships among input variables and output behavior. The model's novelty lies in pioneering the application of advanced metaheuristic algorithms, notably 3mSOS, in pile foundations—a distinctive contribution to geotechnical engineering. This research holds significant promise for enhancing the efficiency and accuracy of pile design in high-rise buildings, thereby bolstering the overall reliability of foundation design. • 3mSOS-LMNN transforms large bored pile foundation design, ensuring precise load-displacement predictions. • SHAP analysis pinpoints parameters influencing pile head displacement, aiding informed decision-making. • PDPs provide a profound understanding of influential parameters, emphasizing their crucial role in pile behavior. [ABSTRACT FROM AUTHOR]