Agriculture in India plays a critical role in sustaining livelihoods, yet faces multifaceted challenges such as unpredictable crop failures, insufficient dissemination of agricultural knowledge, and limited access to advanced technologies. Addressing these challenges requires a multifaceted approach that includes enhancing seed quality and improving predictive accuracy. This chapter explores the application of three advanced Machine Learning (ML) models namely Long Short-Term Memory (LSTM), Moving Average (MA), and Auto Regressive Integrated Moving Average (ARIMA), specifically in the context of agricultural sustainability at the Regional Agricultural Research Station (RARS) in Kottayam, Kerala. The study employs rigorous time series analysis and prediction techniques using ARIMA and MA models to uncover intricate seasonal patterns and evolving trends in seed quality parameters. By leveraging historical data and environmental variables unique to the Kottayam region, LSTM models are utilized to forecast crop yields and optimize farming practices. The integration of these models into a comprehensive web-based service is discussed, aimed at providing practical decision support tools for farmers. This service includes a crop recommendation system and a fertilizer recommendation system. These systems harness the power of ML algorithms to predict optimal crop varieties, recommend suitable fertilizers based on soil and environmental conditions. This chapter underscores the transformative potential of integrating cutting edge ML techniques—ARIMA, LSTM, and MA models—in revolutionizing agricultural practices in India. By improving seed quality and prediction accuracy, these technologies facilitate higher crop yields, enhanced sustainability, and resilience against environmental challenges. The adoption of these advanced models not only benefits farmers by providing actionable insights and precise predictions but also contributes to the economic and environmental sustainability of the agricultural sector in India.