Respiratory diseases constitute a major global health burden, necessitating accurate and reliable diagnostic support tools. Conventional auscultation, despite its widespread clinical use, remains inherently subjective and susceptible to inter-observer variability. In this study, we propose a unified deep learning framework for the automated classification of respiratory sound recordings into four clinically relevant categories: Normal, Crackles, Wheezes, and Crackles + Wheezes. The experimental evaluation was conducted on a publicly available dataset comprising heterogeneous respiratory recordings collected from both patients with pulmonary pathologies and healthy individuals. All audio signals were subjected to standardized preprocessing procedures to enhance signal consistency and ensure reliable feature extraction across acquisition conditions. To ensure methodological rigor and prevent optimistic bias, a strict subject-independent validation strategy was adopted using 5-fold GroupKFold cross-validation based on patient identifiers. Six deep learning architectures were systematically implemented and comparatively evaluated under a controlled and reproducible training protocol, including convolutional (1D-CNN, Deep-CNN), recurrent hybrid (CNN–LSTM, CNN–BiLSTM), and attention-based (CNN–Attention, CNN–Transformer) models. Performance metrics were reported as mean ± standard deviation across folds. The CNN–Attention architecture achieved the best overall performance, yielding a Balanced Accuracy of 90.1% ± 1.8% and a macro F1-score of 89.7% ± 2.1%, demonstrating stable inter-patient generalization. These findings indicate that attention-enhanced hybrid architectures effectively capture both local spectral structures and long-range temporal dependencies inherent in respiratory signals. The proposed framework provides a robust foundation for subject-independent automated lung sound classification and contributes to the development of clinically reliable decision-support systems.