This work employs novel techniques to measure soil water content, an important aspect of agricultural development and water resource management. We used a variety of machine learning models, to examine a large amount of soil moisture data that was gathered over time. In the beginning, we used tools like pandas, matplotlib, and scikit-learn to analyze and display the data extensively. We also used spectral intensity analysis to examine the relationship between soil moisture and several environmental conditions. This research checks how well different computer models predict soil moisture. We also try to make the soil moisture predictions better by using smart techniques and computer algorithms. We look into using distributed control systems to keep an eye on soil moisture in different farming areas. This study is part of a bigger idea called precision agriculture, where we use smart systems to analyze soil moisture data accurately. The results can help farmers make better choices, manage water well, and take care of the environment.