Plant disease classification is crucial for agricultural productivity as it enables timely detection and management of diseases that affect crop yields. Traditional disease prediction methods often rely on manual observation, which can be subjective and time-consuming, Tomato (Solanum lycopersicum) is a highly significant crop globally, but it faces substantial threats from various diseases that can severely impact yield and quality. Effective disease management is essential for sustainable tomato production, highlighting the need for advanced technologies in disease detection and classification. Automated systems utilizing machine learning (ML) and quantum-inspired algorithms have emerged as promising approaches to address these challenges. Current disease detection methods involve visual inspection by trained agronomists, which can be labor-intensive, subjective, and prone to human error. In recent years, machine learning-based approaches have revolutionized disease prediction in agriculture. Techniques such as Convolutional Neural Networks (CNNs) have shown remarkable success in automated disease identification by analyzing digital images of plant leaves. These models utilize deep learning to extract intricate patterns and features from images, enabling accurate disease classification.