Electrocardiogram (ECG) signals offer valuable insights into cardiac health. This paper aims to develop an AI-powered system that enhances ECG biosensors with anomaly detection and predictive modeling. The early recognition of cardiac abnormalities is essential for optimal patient care, and AI provides automatic analysis and interpretation to ECG signals. Using the MIT-BIH Arrhythmia Database, we pre-process ECG signals for model training. For anomaly detection, we have used autoencoders to reconstruct normal ECG patterns and identify anomalies and k-means clustering to detect outliers. For the predictive modeling portion, we used Random Forest regressors for future ECG value prediction, and to capture temporal patterns LSTM networks are used. We evaluated models using metrics such as AUC, precision, recall, and F1-score for anomaly detection, and mean squared error and R-squared for predictive modeling. Where the autoencoder achieved an AUC of 100% for the anomaly detection and the random forest regressor demonstrated a R-squared (R2) score of 0.976. Our findings demonstrate the effectiveness of the proposed AI models in enhancing ECG biosensor functionality. The autoencoder and k-means is used in anomaly detection. The Random Forest and LSTM models exhibited promising predictive capabilities. This study focuses on the effective outcomes of biosensors with the integration of AI powered systems. The proposed models demonstrate the high accuracy in both anomaly detection and predictive modeling, paving the way for smarter and more reliable cardiac diagnostics in healthcare applications.