The proposed work aims to address prescription discrepancies, a significant challenge in global healthcare, with approximately one in thirty patients affected by errors, as reported by the World Health Organization. To tackle the issue, study presents a comprehensive approach to developing a framework that can predict diseases based on patient symptoms and recommend various medications by employing an ensemble machine learning method. The proposed model is trained on a dataset of diseases, symptoms, and associated medications, and an ensemble method called Bagging (Bootstrap Aggregating) is used, which enhances model stability, reduces variance, and improves accuracy. Bagging also prevents overfitting, especially for high-variance models like the Decision Tree Classifier. The result shows substantial improvements in recommendation compared to conventional single-model approaches like the Decision Tree Classifier which achieved 71.13% accuracy which increases to 98.91% after bagging, demonstrating its efficacy. However, it doesn’t show any improvement in model like Multinomial Naive Bayes which achieved 96.72%, which is already a low-variance model that handles categorical symptom data, boosting model performance on textual or labeled inputs. The model’s predictions are validated using metrics such as F-1 score, precision, and recall. Additionally, a Graphical User Interface (GUI) was developed using the Tkinter library, allowing symptom selection via checkboxes to overcome the errors caused by misspellings or incorrect inputs. The work has implications for personalized healthcare and telemedicine platforms, which will provide a scalable, efficient, and user-friendly tool to assist clinicians and patients.