This paper presents an AI-driven telemedicine system for remote lung disease diagnosis through multi-channel respiratory acoustic analysis. The system integrates spatially distributed sensors to capture respiratory sounds, processed via a hybrid deep learning framework combining Convolutional Neural Networks for spectral feature extraction and Recurrent Neural Networks for temporal pattern recognition. Differential noise spectra analysis isolates pathological signatures by emphasizing low-amplitude spectral components through logarithmic normalization, while adaptive thresholding (3–6 dB above RMS baselines) optimizes inhalation-exhalation phase segmentation, achieving 93.4% phase classification accuracy in controlled experiments. To address scalability, the hierarchical architecture employs queuing theory and epidemic spread models for data flow optimization across four tiers: 1.5 GB (individual), 10 TB (clinical), 1,000 TB (regional), and 20,000 TB (national). Grid computing clusters at the secondary tier enable distributed processing, supported by middleware that reduces latency by 41% compared to centralized cloud systems. Parallelized feature extraction pipelines achieve real-time performance (<1.5 s/recording) on embedded hardware, compatible with low-cost sensors (50–4000 Hz frequency range). Validation on synthetic datasets demonstrates 89.2% anomaly detection accuracy for obstructive pathologies (e.g., chronic obstructive pulmonary disease) via spectral divergence thresholds (>15% from healthy references) and robustness to ambient noise (SNR ≥ 8 dB). The system’s modular design ensures compatibility with federated learning frameworks for privacy-preserving distributed diagnostics. By integrating physics-grounded signal processing with scalable grid infrastructure, this work bridges AI innovation and telemedicine deployment needs, offering a solution for large-scale respiratory screening with minimal hardware dependencies.