Agriculture, the backbone of the Indian economy, stands as a cornerstone in the livelihoods of millions, contributing significantly to the economy, providing jobs, and fulfilling the country's food needs. Despite its vital role, the sector faces challenges like inadequate access to real-time weather forecasts, inadequate water management, inaccurate fertilization practices, and a lack of data-driven insights for crop management that inhibit its growth and efficiency. This paper presents a novel solution, the Integrated Agricultural Decision Support System (IADSS), incorporating agricultural practices with machine learning capabilities to establish a comprehensive platform tailored for farmers. By employing advanced meteorological data and state-of-the-art algorithms, this approach delivers accurate, location-specific weather predictions, boosting well-informed decisions regarding planting, irrigation, and harvesting. IADSS surpasses weather forecasting; it extends its utility by delivering guidance on crop selection based on historical data and predicting optimal fertilizers using machine learning models. The proactive features of the system delegate farmers to adopt preventive measures, reducing the need for chemical interventions and promoting sustainable agriculture. Accessible through smartphones, tablets, and computers, the user-friendly web application furnishes farmers with data-driven insights for crop cultivation and fertilizer management. Ultimately, this initiative aims to improve global agricultural productivity, sustainability, and resilience in a changing climate. The research shows commitment to elevating farmers’ livelihoods worldwide by providing necessary tools for success in an ever-evolving agricultural landscape.