Food, shelter, and clothing are essential needs for human beings, with food being a constant requirement that depends on farming. However, unpredictable weather and climate change are making farming increasingly difficult. Traditionally, farmers check various factors like soil pH, moisture, temperature, and humidity before planting crops. They often grow two crops a year, but climate change has complicated even the planning of the first crop. Rainfall is critical for farming, but changing patterns can lead to crop failures and financial losses. Effective crop planning is vital to avoid these risks, yet it often relies heavily on a farmer's experience, which may not always be reliable. Crop sequencing, or crop rotation, is a strategic way to plan crop cultivation over time to maximize resource use and improve soil health. This research explores how machine learning can help predict rainfall and optimize crop sequencing for various crops, including rice, maize, soybeans, and more. By analyzing factors such as temperature, humidity, and soil pH, machine learning offers valuable insights that can help farmers make better decisions, reduce risks, and increase profits. The findings suggest that machine learning can significantly improve agricultural productivity by addressing challenges like unpredictable rainfall and water scarcity. Future research should aim to integrate real-time data, consider additional climate variables, and develop user-friendly tools for farmers to adopt these innovative practices more widely.