India, a prominent global producer of tomatoes, plays a vital role in meeting the worldwide demand and supporting the livelihoods of farmers. Tomatoes are rich in essential vitamins and antioxidants, contributing to food security. Tomato production has been negatively affected by climate change, poor water management, and excessive pesticide use, resulting in reduced yields. To tackle these issues, we suggest the implementation of an advanced deep convolutional neural network with tailored machine learning techniques to identify and categorize weeds, pests, and diseases that impact tomato plants. Through the training and assessment of this model using holdout validation on an extensive dataset of tomato leaves, our goal is to achieve higher validation accuracy and enhance current methodologies in the analysis of tomato plants.