Medical imaging datasets are essential for modern healthcare, enabling the diagnosis and treatment of various diseases. However, interpreting medical images poses significant challenges due to factors like image quality and anatomical variability. Conventional methods often rely on manual feature extraction and simplistic classifiers, which may not fully capture the complexity of medical imaging data. To address this, our research introduces an innovative approach that combines the MixedNet algorithm-based feature engineering with deep learning architectures and an AI-driven classifier. This fusion leverages recent advancements in AI to enhance classifier performance, revolutionizing disease prediction and treatment planning by unlocking the rich diagnostic potential of medical images. Through extensive experimentation and evaluation, our study demonstrates the effectiveness of our methodology in overcoming the limitations of ambiguous and imperfect medical data. Our approach not only achieves superior classification accuracy but also exhibits resilience to noise and interpatient variability encountered in real-world medical imaging datasets. Furthermore, by conducting comprehensive ablation studies, we provide valuable insights into the individual contributions of each component within our framework. In conclusion, our research presents a groundbreaking paradigm for automating medical disease prediction from imaging data, paving the way for a new era of precision medicine where AI-enabled analysis empowers clinicians with unprecedented diagnostic capabilities, ultimately revolutionizing patient care paradigms.