India, known for its rich agricultural history, is a farming nation, and farming is essential to the nation's socioeconomic balance. AgricultureAgriculture has played a significant role in the development of human civilization, but current issues go beyond crop productivity and include maximizing the use of essential resources like water, pesticides, and fertilizers. In this context, machine learning (ML)Machine Learning (ML) becomes a powerful tool that gives farmers customized advice on how to increase output in a sustainable way. This chapter explores the intersection of MLMachine Learning (ML) and agricultureAgriculture, focusing on Linear RegressionLinear Regression (LR), Support Vector Machine (SVM)Support Vector Machine (SVM), Gaussian Process Regression (GPR)Gaussian Process Regression (GPR), Kernal Regression and Decision TreeDecision Tree (DT) algorithmsDecision tree algorithm. These models adeptly capture the intricate dependencies between variables, facilitating accurate predictions of suitable fertilizer varieties. Factors such as temperature, humidity, soil type, crop variety, and nutrient content are meticulously considered. The Decision TreeDecision Tree (DT) model, with an excellent \(R^{2}\) of 0.306, stands out as particularly adept in navigating diverse climatic and field conditions, thereby offering invaluable guidance to farmers striving for optimal resource management and crop cultivation. Moreover, this chapter integrates insights on materials utilized in agricultureAgriculture, emphasizing their pivotal role in sustainable farmingSustainable farming practices.