Abstract
By using machine learning approaches, the AI-powered personalised drug recommendation system solves the problems of accurate prescription recommendations and access to specialist treatment. Sometimes depending mostly on manual procedures, traditional healthcare systems might lead to prescription mistakes and delays in the delivery of the required treatment. Conversely, in this work, the Random Forest and XGBoost models are applied to match symptoms to diseases and suggest appropriate medications with an accuracy of more than 97%. By means of real-time APIs and web scraping techniques, the system offers customized doctor and pharmacy suggestions based on the location of the user, therefore bridging the gap between suggested cures and healthcare providers. To produce a smooth, unique healthcare experience, the system gathers user-specific data like symptoms, allergies, age, sex, activity level, and severity of symptom. The simple interface of the system and its report-generating tool, which highlights particular medications and healthcare providers, seek to improve accessibility, lower mistakes, and support informed healthcare decisions.