Precision agriculture has gained significant attention in recent years, aiming to optimize farming practices through data-driven decision-making. This paper presents an IoT-based crop recommendation system that utilizes real-time soil data collected using an ESP32 microcontroller and various sensors, including a DHT11 for temperature and humidity, a soil moisture sensor, a TDS sensor, and a pH meter. The collected data is transmitted to the cloud for further analysis using Fire base. A machine learning model processes the data to provide accurate crop recommendations, improving agricultural productivity. This study focuses on developing a cost-effective and scalable solution suitable for small and medium-scale farmers