Background: Diagnosing lung diseases including COVID-19 has become crucial. AI-based methods offer rapid chest X-ray image analysis. Method: The COVID-19 Chest X-ray dataset was pre-processed using Improved Anisotropic Diffusion Filtering (IADF). Features were extracted via GLCM, uLBP, HoG, and hvnLBP. Adaptive Reptile Search Optimization (ARSO) was used for optimal feature selection. A hybrid model—Multi-head Attention-based Bi-GRU with Deep Sparse Auto-encoder (MhA-Bi-GRU with DSAN)—performed multiclass classification. Dynamic Levy-Flight Chimp Optimization (DLF-CO) minimized the loss function. Results: Using Python simulations, accuracy reached 0.95% at 0.001 learning rate and 0.98% at 0.0001. Conclusion: The hybrid deep-learning method demonstrated superior disease classification performance on chest X-ray images.