Around the globe Heart disease is still one of the top causes of death, which highlights the importance of early and precise predictions to enhance patient outcomes. Technologies have demonstrated impressive potential in the field of medical diagnostics especially convolutional neural networks (CNNs). Yet, issues like finding the best features to use, understanding how these models work, and ensuring they run efficiently remain obstacles to their broader use in clinical settings. This review explores the advancements in Adaptive Ensemble Serial Cascade Convolution Networks for Predicting Heart Diseases in Early Stage (Ada-EnSCCNet) emphasizing their role in enhancing classification accuracy and model robustness. We analyse various deep learning architectures that integrate ensemble learning, serial cascade frameworks, and adaptive mechanisms to improve feature extraction and decision-making processes. The effectiveness of hybrid models is also examined. Special attention is given to feature selection techniques that enhance model performance while mitigating over fitting risks. Through a comparative analysis of existing studies discuss about the strengths and limitations of different CNN-based ensemble strategies, highlighting key challenges such as data imbalance, interpretability, and computational complexity. Additionally, in this review paper its going to identify emerging trends, including the integration of AI, ML real-time predictive analytics, and multi-modal data fusion, which could further optimize heart disease diagnosis. This review gives a detailed look at the latest advancements in ensemble models that use deep learning for predicting heart disease. It provides crucial information on prospective avenues for future study and useful applications in the healthcare industry.