With the rapid population growth of the 20th and 21st centuries, there is an increasing need for improved agricultural management to meet the food demands of this growing population. To address this challenge, the integration of sensors and embedded systems has been widely employed to enhance productivity and optimize resource utilization for meeting the food demands of this growing population. This research aims to develop a sophisticated system capable of collecting essential data on crucial parameters such as the temperature of the soil, temperature of the ambient air, the humidity of the soil, and humidity of the ambient air from the agricultural field. The data will be collected using Microcontrollers and stored in a centralized database. Leveraging this data, the system intends to create detailed two-dimensional maps utilizing Matplotlib and Gaussian Interpolation techniques, effectively portraying the current state of the agricultural field. To ensure seamless information transmission, the microcontroller necessitates remote communication with the server through ESP32, equipped with long-range radio frequency transmitters. This efficient combination ensures reliable data transfer between the microcontroller and the server, facilitating the smooth operation of the system. By integrating embedded systems, Python data processing, and Interpolation, the proposed system provides a reliable tool for data-driven agriculture. This approach will empower farmers with actionable insights, leading to more efficient resource allocation and sustainable farming practices.