Abstract : This paper proposes a methodology leveraging Data Science and IoT to enhance precision farming practices in the old Mysuru region villages. The aim is to optimize agricultural yield and resource management through advanced technology integration. The proposed system utilizes IoT sensors for real-time data collection on soil parameters such as nutrient levels, moisture content, and temperature across 500 farm lands. These sensors, including the JXCT Soil NPK Sensor, facilitate continuous monitoring and data acquisition, overcoming the limitations of traditional periodic sampling. Data Science techniques are employed to analyze the collected data, utilizing machine learning algorithms to derive actionable insights. This analysis aids in understanding soil health dynamics, predicting crop yields, and optimizing fertilizer and water usage. The system also incorporates an Android-based mobile application for seamless data visualization, remote monitoring, and decision support for farmers. The block diagram illustrates the architecture of the proposed system, highlighting the integration of IoT devices, cloud-based data storage, and machine learning models. Data flows from the sensors to the cloud, where it undergoes preprocessing, analysis, and storage. Farmers access the processed information through the mobile app, enabling informed decision-making in real time. This approach aims to transform traditional farming practices into data-driven precision agriculture, enhancing productivity, sustainability, and economic viability in the old Mysuru region villages. The effectiveness of the proposed methodology is validated through the analysis of 500 datasets, demonstrating its potential to revolutionize agricultural practices in the region.