Agriculture remains to be a critical industry for the majority of countries. It is the primary source of food for the whole world's population. It does, however, confront a significant challenge: producing more and better while also boosting sustainability via prudent use of natural resources, decreasing environmental damage, and responding to climate change. As a result, it is critical to the transition from old to contemporary farming practices. Precision agriculture is one of the options for addressing the increased need for food while also maintaining sustainability. The importance of information is growing in precision agriculture. Information about weather conditions, soils, illnesses, insects, seeds, fertilizers, and other factors contribute significantly to the sector's economic and long-term development. The collection, transmission, selection, and analysis of data are all part of precision management. As the amount of agricultural data grows, powerful analytical approaches capable of processing and evaluating enormous volumes of data to acquire more trustworthy information and much more precise forecasts are becoming increasingly important for handling real-time data analysis with vast data. Data mining is projected to play a critical role in precision agriculture. This study aims to analyze current studies and research on precision agriculture that use the modern technique of data mining to tackle some agricultural issues.