Brinjal (eggplant) fields are susceptible to numerous leaf diseases that negatively impact their health and production. In response to this, we create an AI supported Brinjal Leaf Disease Detection and Fertilizer Recommendation System that employs deep learning and generative AI for the purpose of disease identification and fertilizer application by farmers. This model employs ResNet50 and MobileNet convolution neural networks for classifying brinjal leaf diseases with a high degree of accuracy. ResNet50 is a deep residual neural network that guarantees high accurate disease detection. MobileNet is a lightweight CNN that can be deployed to mobile and edge. Comparative analysis is carried out between ResNet50, MobileNet based accuracy. ResNet50 has a 86% validation accuracy and MobileNet has a 96% validation accuracy. With assistance of generative AI tool named Gemini AI is utilized to make fertilizer recommendations based on various agricultural parameters [the nature of the disease, soil type, temperature, humidity, moisture, and soil nutrients (NPK)]. The generative AI processed the existing data and provided customized fertilizer application recommendations for recovery of crops and soil health in an environmentally sustainable and efficient way. The system maintains privacy of efficiency, By integrating brinjal leaf disease detection and Gemini AI based fertilizer recommendation, this system equips farmers with actionable knowledge, minimizes crop loss, and fosters sustainable agriculture.