This study contributes to ongoing efforts in agricultural technology by advancing the application of artificial intelligence in poultry disease classification, crucial for sustaining agriculture amidst global demographic changes. Focusing on the Chicken Disease Image Classification task, the research evaluates and fine-tunes state-of-the-art Convolutional Neural Networks (CNNs) like EfficientNet and ConvNeXT, alongside Vision Transformers (ViTs) such as DeiT and Swin Transformer. These models, pretrained on ImageNet, are adapted to classify various poultry diseases from chicken fecal matter images, representing conditions like Coccidiosis, Healthy, New Castle Disease, and Salmonella. The study juxtaposes these advanced models with traditional machine learning approaches to assess their relative efficacy in handling class imbalances and complexities inherent in the dataset. Through a comprehensive evaluation using Precision-Recall curves and Confusion Matrices, the research sheds light on the nuances of model performance in disease identification. The findings underscore the challenges posed by imbalanced datasets and emphasize the need for balanced data in training machine learning models for disease classification. This work showcases the capabilities of current state of the art AI in agricultural settings, aiming to enhance early disease detection tools for poultry health management, especially beneficial in resource-limited environments.