The detection of cardiac abnormalities using electrocardiogram (ECG) signals is crucial for early diagnosis and intervention in cardiovascular diseases. Traditional deep learning models often lack adaptability to varying signal patterns. This study introduces the Neuro-Informed Adaptive Learning (NIAL) algorithm, a hybrid approach integrating convolutional neural networks (CNNs) and transformer-based attention mechanisms to enhance ECG signal classification. The algorithm dynamically adjusts learning rates based on real-time validation performance, ensuring efficient convergence. Using the MIT-BIH Arrhythmia and PTB Diagnostic ECG datasets, our model achieves high classification accuracy, outperforming conventional approaches. These findings highlight the potential of NIAL in real-time cardiovascular monitoring applications.