This study explores the potential of integrating photoplethysmogram (PPG) and electrocardiogram (ECG) signals for non-invasive heart failure evaluation. Using a dataset from the MIMIC-III database, which includes 682 heart failure patients and 954 controls, the researchers focus on continuous, non-invasive monitoring. Key features such as QRS interval, RR interval, augmentation index, heart rate, systolic and diastolic pressure, and peak-to-peak amplitude are selected for their clinical relevance. Machine learning algorithms, particularly Random Forest, were used to train the model, achieving impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). The integrated approach, combining PPG and ECG signals, outperforms single-signal strategies, highlighting its potential in early and precise heart failure diagnosis. The study also emphasizes the importance of continuous monitoring with wearable technology, suggesting significant advancements in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems, enabling continuous monitoring and aiding in early detection and prevention of critical health conditions.This study explores the potential of integrating photoplethysmogram (PPG) and electrocardiogram (ECG) signals for non-invasive heart failure evaluation. Using a dataset from the MIMIC-III database, which includes 682 heart failure patients and 954 controls, the researchers focus on continuous, non-invasive monitoring. Key features such as QRS interval, RR interval, augmentation index, heart rate, systolic and diastolic pressure, and peak-to-peak amplitude are selected for their clinical relevance. Machine learning algorithms, particularly Random Forest, were used to train the model, achieving impressive accuracy (98%), sensitivity (97.60%), specificity (96.90%), and precision (97.20%). The integrated approach, combining PPG and ECG signals, outperforms single-signal strategies, highlighting its potential in early and precise heart failure diagnosis. The study also emphasizes the importance of continuous monitoring with wearable technology, suggesting significant advancements in non-invasive cardiovascular health assessment. The proposed approach holds promise for implementation in hardware systems, enabling continuous monitoring and aiding in early detection and prevention of critical health conditions.