AgriSage is an revolutionary invention for agriculture because this incorporates the latest artificial neural learning techniques, as well as dynamic network construction to provide farmers with accurate crop advice. This proposed work combines agronomic factors including the soil type, climate, past production records, and geographical information of the specified region. AgriSage delivers custom advisory on appropriate crops to grow. The platform uses Flask web application for the base, for this reason; it will be easy to use and accessible by everyone having no prior knowledge in technology. The system enhances the decision-making process for the farmers so that they can improve their yields, reduce wastage of resources, and come up with ways to counteract the environmental impacts in future. Furthermore, even though it operates in the agricultural sector, this resource has a friendly layout that breaks the gap between the conventional farming world and the richer technological world. The proposed machine learning (ML)-based User Interface (UI) accepts input values from the user, such as nitrogen levels, phosphate levels, temperature, pH levels, humidity, rainfall levels, and a few other parameters, and processes the data to predict the most suitable crop recommendation. This work compares 10 similar ML models to identify the best-performing, high-accuracy algorithm for recommending the optimal crop for cultivation.