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This study presents an innovative AI-driven approach to enhancing and visualizing ECG data using fusion image processing techniques. Discrete Wavelet Transform (DWT) reduces noise, while Principal Component Analysis (PCA) improves feature extraction for accurate cardiac diagnosis. The proposed Hippopotamus fine-tuned Adaptive Long Short-Term Memory (HALSTM) model effectively captures complex patterns, achieving high accuracy (98.8%), recall (95.26%), and precision (98.56%). MATLAB simulations validate its efficacy, leveraging dynamic time-series plots, heat maps, and spectrograms for advanced visualization. This work aims to enhance cardiovascular disease monitoring, offering a cost-effective, comprehensive solution to improve medical care and save lives.