<p>Detecting bird species from environmental acoustic signals presents a significant problem of noise interference, which diminishes the quality of extracted features and limits model reliability. To address this problem, a spectro-temporal deep CNN–BiLSTM framework is proposed here for recognizing bird species from environmental audio data with a solution in advancement in existing approaches for effectively modeling both local spectral and sequential temporal patterns. The CNN component extracts discriminative spectral features, while the BiLSTM layer captures long-term temporal dependencies, preserving the dynamics of bird calls in acoustically complex environments. Solution of already existing approaches. Differing from existing studies that primarily use clean or uniformly noisy data, this study thoroughly analyzes the proposed model’s originality and novelty in its noise robustness, convergence characteristics, and training efficiency across diverse controlled acoustic scenarios. Performance is assessed using classification accuracy, convergence speed, and training time as evaluation metrics. Numerical findings of proposed approach achieves 98.12% classification accuracy and completes processing in 95.02&#xa0;s on noise-free acoustic bird calls with reduced number of training epochs to convergence, while ensuring reliable performance in acoustically challenging environments. The results indicate that the integration of bidirectional temporal modeling enhances robustness to acoustic variability without increasing computational complexity. The proposed spectro-temporal CNN–BiLSTM approach effectively balances performance accuracy, robustness to noise, and computational efficiency, enabling its reliability in the field and scope of multimedia-based bioacoustics monitoring.</p>