This study investigated the impact of machine learning algorithms on improving decision-making processes in industrial companies in Hebron and Bethlehem, Palestine. A descriptive-analytical approach was employed to examine the study variables within their organizational context. The sample included (135) administrative employees, of whom (126) completed the questionnaire, yielding a 92.3% response rate. Data were collected using a structured questionnaire to ensure reliability and accuracy. The results indicated that both the use of machine learning algorithms and their contribution to improving decision-making processes were at a high level. Decision-making processes had the highest relative weight (77.77%), followed by machine learning algorithms (74.35%). Hypothesis testing confirmed a positive and statistically significant effect of machine learning algorithms on enhancing decision-making (β = 0.591, p = 0.000). The study also revealed that demographic factors influenced perceptions and application of machine learning. Participation in artificial intelligence and computer science training significantly affected both algorithm usage and decision-making improvement (F = 36.152, p = 0.000). Gender (F = 5.857, p = 0.017) and years of experience (F = 2.701, p = 0.049) also had significant effects, whereas educational qualifications and specialization showed no significant impact. In conclusion, the findings demonstrate that machine learning algorithms are vital for enhancing managerial decision-making in industrial companies. The results emphasize the importance of professional training in artificial intelligence and computer science, as well as the practical application of modern technologies to optimize decision-making processes in industrial settings.