An essential component of a nation's economic growth is its agriculture sector. In developing nations like India, agriculture is one of the major sources of income for many people. Indian farmers face immense challenges due to unpredictable weather patterns, leading to difficult decisions about crop cultivation and tragically contributing to a rising number of suicides. This not only threatens individual livelihoods but also poses significant economic risks, as agriculture is a cornerstone of the Indian economy and a major employer. Additionally, the use of excessive fertilizers to meet food demand risks soil degradation and contamination, further exacerbating agricultural sustainability issues. Addressing these challenges requires a holistic approach, including support for farmers, sustainable farming practices, and policies to mitigate climate impacts. Protecting farmers and ensuring agricultural sustainability is crucial for India's prosperity and stability. This paper recognizes the transformative potential of technology and aims to leverage it for the betterment of Indian agriculture. So, we came up with the approach which helps the farmer by giving crop predictions based on analysis of the factors like temperature, Rainfall, Crop production, yield and prices by using Machine Learning Algorithms like – Logistic Regression, Random Forest and Decision Tree. By leveraging these Machine learning algorithms and analysing factors, you can create a robust crop prediction system that assists farmers in making data-driven decisions to optimize their yields and profitability. Additionally, it's essential to continuously evaluate and refine the models based on new data to ensure their accuracy and relevance over time.