In order to monitor and control plant health, there is a growing interest in utilising artificial intelligence (AI) technology due to the increasing need for sustainable agricultural practices and the necessity to maximise crop output. In this paper, we provide a novel method for identifying and resolving leaf-related problems in tomato plants by applying AI-driven solutions. The creation of a reliable AI-based leaf detecting system is the first component of our suggested approach. Through the use of deep learning techniques and convolutional neural networks, our goal is to develop a highly accurate model that can accurately recognise many kinds of leaf abnormalities, such as pests, illnesses, and nutrient deficits. In order to analyse leaf images taken by high-resolution cameras placed in the field or greenhouse, this system will make use of image processing techniques. Moreover, our methodology goes beyond simple identification to include corrective actions. After detecting anomalies in leaves, the artificial intelligence system will suggest focused actions according to the particular problem found. These treatments could involve adjusting irrigation schedules, light exposure, or other environmental factors, as well as applying fertilisers, insecticides, or other agronomic inputs precisely. We will carry out extensive field testing in tomato cultivation environments to verify the efficacy of our suggested remedy. We will evaluate the efficacy and precision of the AI-driven leaf detection system in quickly and reliably diagnosing plant health problems through practical experimentation. We will also assess how the suggested remediation techniques affect plant health in general, disease resistance, and productivity. In the end, proactive and targeted management techniques made possible by the application of AI-driven enhanced solutions for plant leaf identification and remediation have the potential to completely transform contemporary agriculture. Modern agricultural techniques combined with established ones can increase crop yields, cut down on resource waste, and support the long-term viability of food production systems.