This study explores recent transformative advancements in deep learning-based healthcare technologies, revolutionizing patient care by enabling personalized precision medicine and extending access through IoT and cyber-physical systems. Diagnostic methodologies for critical conditions like cancer, diabetes, and heart failure are examined, utilizing computational intelligence to enhance patient identification and treatment. The focus lies on electronic health records (EHRs), investigating contemporary deep learning techniques for cancer prediction and framework-based mechanisms for healthcare optimization. Key algorithms like SVMs, Autoencoders, and CNNs are explored, showing applicability in clinical settings and genomic sequence-based diagnostics. Healthcare's unique processing techniques, spanning gene-based strategies, clinical tests, observation, and diagnostic models, are scrutinized for predictive and treatment potential. Integration of statistical and medical references is highlighted for efficient predictions alongside patient-specific data. The research advocates for AI-powered DSS integration, with CNNs as potent tools for customized medical interventions, marking a significant step towards elevated patient care and precision medicine realization.