The pursuit of food security has led to the exploration of sustainable agricultural methods, particularly focusing on advanced techniques for identifying plant diseases. This review paper examines the use of evolutionary algorithms and deep learning to improve the accuracy and efficiency of plant disease detection systems. It provides a detailed background on the shortcomings of traditional methods and the potential benefits of new technologies. By thoroughly reviewing existing literature, the paper analyzes the methodologies used in various studies, showcasing the adaptability and effectiveness of these techniques in different agricultural settings. The results indicate a notable enhancement in the accuracy and efficiency of plant disease detection, supporting more effective agricultural practices. The paper emphasizes the importance of incorporating these advanced technologies into agricultural informatics to promote environmental sustainability and food security. It also discusses future research directions, highlighting the need for ongoing innovation in sustainable agriculture.