The sustenance of the world’s expanding population relies heavily on agricultural productivity, wherein the well-being of crops plays a pivotal role in attaining this objective. Tomatoes, scientifically known as Solanum lycopersicum, are a highly significant crop in terms of consumption and economic value. Human nutrition depends on them a lot and they also contribute significantly to economic activity. Nonetheless, tomato plant diseases are a major threat to both the quantity and quality of this crop. The present review provides a comprehensive analysis of the latest advancements in identifying tomato leaf diseases using machine learning models. In this discussion, the focus is on key researches carried out within 2022 and 2023 that have exhibited effectiveness of different models among them pre-trained CNNs, EfficientNetV2B2, YOLO v5, and attention mechanisms. They have been found to be very accurate in diagnosing diseases. Thereafter, this study applies the outcomes gained herein to establish an initial framework for automatically detecting tomato leaf diseases. It integrates image processing techniques with ensemble machine learning using Multiclass Support Vector Machine (SVM). However, preliminary findings suggest that early disease detection can be made more dependable and effective. Nevertheless, constraints such as need for high-quality datasets, significant computational resources and complex model architecture should be explored further in order to overcome these constraints. This study seeks to present an efficient method that can easily be expanded upon for enhancing tomato leaf disease detection. This approach therefore aims at avoiding restrictions imposed by conventional methods thereby contributing to agricultural technology advancement too.