ABSTRACT The persistent emergence of poultry epidemics (e.g., Newcastle disease) jeopardizes operational stability and sustainability in commercial poultry production systems. Current diagnostic approaches for broiler diseases predominantly rely on subjective clinical assessments. These methodological limitations compromise operational efficiency through diagnostic delays and production chain disruptions, requiring automated detection systems capable of real-time pathological evaluation. A baseline Random Forest (RF) model achieved 94.01% diagnostic accuracy for broiler diseases. To optimize performance, we developed RF_WOA_DBO-an integrated algorithm combining RF with enhanced Whale Optimization Algorithm (WOA) for global feature selection and modified Dung Beetle Optimizer (DBO) for local parameter tuning. The optimized parameters were subsequently implemented in the RF classifier training. The composite algorithm reduced feature redundancy by approximately 30% while ensuring the effective retention of critical diagnostic indicators. The RF_WOA_DBO hybrid model achieved an accuracy of 98.29%, representing a 4.28% improvement over the baseline RF model. Comparative analysis revealed that traditional PCA methods risk losing essential pathological features by disregarding nonlinear data relationships, whereas deep learning requires substantial computational resources and high-quality datasets. In contrast, RF_WOA_DBO provides computationally efficient solutions suitable for resource-constrained poultry farming environments. This study introduces a novel methodology for broiler disease diagnosis and prediction, substantially improving accuracy and efficiency while maintaining low computational costs. The proposed framework can be seamlessly integrated into IoT-based broiler health monitoring platforms, offering valuable theoretical foundations and technical support for disease detection and prevention in poultry farming.