Audio classification has become a notable area of interest, particularly for its promising uses in the domain of poultry farming, where early detection of health issues is crucial. However, there is a lack of research on using deep learning techniques for this purpose. This study aims to fill this gap by exploring deep learning’s potential for detecting various health issues in poultry. We propose a ma chine learning approach using audio data, collected from healthy and unhealthy birds, including vocalizations, breathing patterns, and movement noises. After preprocessing and feature extraction, machine learning models were trained to classify audio signals into healthy and diseased categories. The evaluation metrics, including accuracy of 0.9423, precision of 0.88, recall of 0.80, and F1-score of 0.8380, were used to demonstrate promising results. This research contributes to developing non-invasive and cost-effective methods for early disease detection in poultry, enhancing animal welfare and industry sustainability.