One of the major diseases causing deaths worldwide, even though a very crucial region that calls for early screening to help with the outcome from treatment is cancer. Classical diagnostic approaches such as carrying out a biopsy or a scan can be quite challenging and therefore time-consuming by requiring extensive expertise and which may introduce variability in such results as well as making the delay in reaching diagnoses. This paper proposes an integrated AI solution using deep learning technologies, a residual neural network (ResNet)-based model for detection of multiple types of cancers: brain tumors, lung cancer, and cervical cancer from medical images. It emphasizes the development of an interactive web-based frontend along with an NLP-powered chatbot for the effective engagement of patients. This will make it easy for patients and other health professionals to access and interpret diagnostic results. The chatbot will give interactive support in the form of questions and answers on diagnoses and treatment options. The integration of advanced diagnostic capabilities with user-centered design addresses the gap of AI technologies’ integration into clinical workflows. This aims to create a holistic system improving the speed and accuracy of cancer detection while enhancing patient care and understanding, hence creating a more informed and engaged patient population.