Cardiovascular diseases (CVDs) remain one of the leading causes of mortality worldwide, highlighting the need for accurate and timely diagnostic approaches. Electrocardiogram (ECG) signals provide valuable information regarding cardiac activity and are widely used for the detection of heart abnormalities. However, traditional diagnostic methods often rely on manual interpretation, which may be time-consuming and subject to inter-observer variability. Recent advances in deep learning have demonstrated significant potential for automated cardiovascular disease detection, yet many existing models operate as black-box systems, limiting their acceptance in clinical practice. To address this challenge, this paper proposes an Explainable Deep Learning Framework for Early Detection of Cardiovascular Diseases Using Electrocardiogram Signals and Clinical Data (AFJBS). The proposed framework integrates ECG signal characteristics with relevant clinical attributes, including age, gender, blood pressure, cholesterol level, and body mass index, to improve diagnostic performance. ECG signals undergo preprocessing and feature extraction before being combined with clinical information and processed through a hybrid deep learning architecture. An explainability module based on feature attribution techniques is incorporated to provide transparent and interpretable predictions for healthcare professionals. The framework is evaluated using publicly available cardiovascular datasets and standard performance metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). Experimental results demonstrate that the proposed AFJBS framework achieves superior classification performance while maintaining model interpretability. The integration of explainable artificial intelligence and multimodal healthcare data supports reliable decision-making and has the potential to assist clinicians in the early diagnosis and management of cardiovascular diseases.