This study explores the separation and optimization of molybdenum (Mo) from mixed mineral acids derived from semiconductor industry waste streams with tributyl phosphate (TBP) by implementing machine learning (ML) models. Numerous tests were performed to evaluate the impact of various operational variables on the effectiveness of Mo extraction and stripping. The SVR-SFLA model yielded the most meticulous predictions, identifying optimal extraction conditions with a TBP concentration, mixing time, temperature, and O/A ratio of 50%, 30 min, 25°C, and 1, respectively, achieving 77.8% efficiency. The derived results, in tandem with the McCabe-Theil diagram, indicated a four-stage counter-current extraction process required to achieve a yield exceeding 99%. For the stripping process, the hybrid model indicated optimal conditions with 3 M NH4OH and an A/O ratio of 0.5 at 50°C during 20 min, requiring two counter-current stages for nearly complete stripping.