This study addresses the urgent need for early detection and intervention in cases of drug addiction by proposing a comprehensive model that integrates questionnaire analysis, blood test analysis, and voice analysis. Leveraging machine learning techniques, questionnaire analysis aids in identifying behavioral patterns indicative of drug addiction. Optical Character Recognition (OCR) facilitates blood test analysis, extracting relevant information efficiently. Voice analysis employs a recurrent neural network model, specifically LSTM, to discern subtle vocal cues associated with drug addiction. Additionally, treatment assistance is offered through a chatbot powered by Natural Language Processing (NLP). By encompassing conventional methods alongside advanced Artificial Intelligence (AI) techniques, this study underscores the pivotal role of artificial intelligence in combatting drug addiction and facilitating timely intervention for individuals in need.