The incubation period for bacteriological cultures is typically between 24 and 72 h, which increases the amount of time that a diagnosis is uncertain. The objective of this research is to develop an intelligent and predictive program that can forecast culture results using preliminary clinical and biological data. 999 samples from the Mohammed VI University Hospital in Oujda during July and September of 2025 were utilized as the basis for this retrospective analysis. Biological, contextual, and demographic factors were among the eighteen variables that were examined. Cross-validation was used to optimize a Gradient Boosting method after a number of machine learning techniques were compared. SHAP analysis was used to assess the model's interpretability. With an areas under the curve of 0.82, sensitivity of 76.50%, accuracy of 76.50%, and F1 score of 77.03%, the Gradient Increasing model performed better. Robustness was confirmed by external validation on time divisions. External validation on time divisions verified robustness. Age, the prescribing department, and microscopic biological markers like WBC MO and RBC MO were the most predictive factors. For the algorithm's clinical use, a software application was created. This method shows that an early microbiological decision support tool is feasible and has good results for improving patient care.