Citrus fruits constitute a vital component of global agriculture and economics, yet they face significant threats from diseases, resulting in substantial annual economic losses for citrus growers. Timely and precise disease detection is imperative for effective disease management strategies. This study presents a novel deep learning-based framework for detecting and classifying citrus fruit diseases using image analysis techniques. The research endeavor begins with the construction of a comprehensive dataset, encompassing meticulous data collection and labeling of various disease categories. Subsequently, a convolutional neural network (CNN) architecture is employed and trained on this dataset. CNNs exhibit proficiency in automatic feature extraction, enabling them to discern intricate patterns directly from image inputs, rendering them particularly suitable for image-based tasks such as disease detection in agriculture. To further enhance the model's performance, transfer learning is leveraged to initialize the model parameters. The proposed model achieves a remarkable accuracy of 97.18% on the test dataset, underscoring its efficacy in automated citrus fruit disease identification. This pioneering approach holds significant promise in assisting citrus growers with timely disease management, ultimately leading to improved citrus fruit yield and quality. Furthermore, the study explores the potential for integrating the developed system into existing agricultural practices, highlighting avenues for future research and application in precision agriculture and remote monitoring systems for crop health.