Poultry farming is a critical component of food security and economic development in Nigeria and across sub-Saharan Africa. However, recurrent outbreaks of multi-disease conditions such as Fowl Cholera, Newcastle Disease, and Coccidiosis continue to undermine productivity. Traditional diagnostic methods, including serology, culture, and PCR, are effective but remain costly and inaccessible to smallholder farmers. Artificial Intelligence (AI) offers scalable solutions for disease prediction, yet most models function as opaque “black boxes,” limiting trust and adoption. This study introduces an Explainable AI (XAI) framework for multi-disease prediction in poultry, leveraging a dataset collected from farms in Osun, Lagos, Oyo, and Ogun States. The dataset integrates environmental, management, and health outcome variables, enabling multi-label classification of disease risks. Gradient Boosting and Random Forest models were trained, achieving an overall accuracy of 92% and F1-score of 0.89. SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) were employed to provide interpretability. Results revealed that humidity below 40%, stocking density above 25 birds/m², irregular vaccination schedules, and proximity to other farms (<500 m) were the most influential predictors of outbreaks. Case-specific LIME explanations demonstrated how individual farm practices contributed to risk, offering actionable insights for farmers and veterinarians. This research highlights the potential of XAI to bridge the gap between advanced predictive models and practical farm-level decision-making. By combining accuracy with transparency, the framework empowers stakeholders to adopt proactive biosecurity measures, ultimately reducing mortality and improving productivity. Future work will integrate IoT sensors and mobile dashboards for real-time disease surveillance.