This paper presents an IoT-based soil pH detection and crop recommendation system that integrates sensor technology with machine learning algorithms to optimize agricultural productivity. The system employs sensors to monitor soil parameters such as nitrogen, phosphorus, potassium, pH, temperature, and moisture, transmitting the data to a cloud-based database for analysis. Machine learning algorithms, including Linear Regression, K-Nearest Neighbor, Decision Tree, Random Forest Regression, Neural Network, Support Vector Machine, and XGBoost, are used to analyze the data and recommend suitable crops based on soil conditions. The system aims to reduce fertilizer use, minimize labor, and enhance crop yields by providing farmers with data-driven insights into soil health and crop selection. The system's architecture includes a hierarchical structure with sensor nodes deployed across agricultural fields, a Wireless Sensor Network (WSN) for data transmission, and a cloud-based platform for data storage and analysis. The pH sensor is a key component, measuring soil acidity or alkalinity by detecting hydrogen ion concentrations. The data is processed and stored, then transmitted to the cloud for further analysis. The cloud platform uses statistical methods and machine learning algorithms to generate crop recommendations based on soil pH and other environmental factors. The system also includes a user interface for farmers to access real-time soil pH measurements, historical trends, and personalized crop recommendations. It enables farmers to make informed decisions about crop selection and fertilizer application, leading to improved agricultural productivity and sustainability. The system's scalability and adaptability make it suitable for deployment across various agricultural settings, from small-scale farms to large commercial enterprises. The integration of IoT sensors, web-based interfaces, and data analytics algorithms empowers farmers to make data-driven decisions, optimize resource allocation, and reduce environmental impact. The system's ability to address soil deficiencies and provide customized fertilizer prescriptions aligns with sustainable agricultural practices and contributes to global food security.This paper presents an IoT-based soil pH detection and crop recommendation system that integrates sensor technology with machine learning algorithms to optimize agricultural productivity. The system employs sensors to monitor soil parameters such as nitrogen, phosphorus, potassium, pH, temperature, and moisture, transmitting the data to a cloud-based database for analysis. Machine learning algorithms, including Linear Regression, K-Nearest Neighbor, Decision Tree, Random Forest Regression, Neural Network, Support Vector Machine, and XGBoost, are used to analyze the data and recommend suitable crops based on soil conditions. The system aims to reduce fertilizer use, minimize labor, and enhance crop yields by providing farmers with data-driven insights into soil health and crop selection. The system's architecture includes a hierarchical structure with sensor nodes deployed across agricultural fields, a Wireless Sensor Network (WSN) for data transmission, and a cloud-based platform for data storage and analysis. The pH sensor is a key component, measuring soil acidity or alkalinity by detecting hydrogen ion concentrations. The data is processed and stored, then transmitted to the cloud for further analysis. The cloud platform uses statistical methods and machine learning algorithms to generate crop recommendations based on soil pH and other environmental factors. The system also includes a user interface for farmers to access real-time soil pH measurements, historical trends, and personalized crop recommendations. It enables farmers to make informed decisions about crop selection and fertilizer application, leading to improved agricultural productivity and sustainability. The system's scalability and adaptability make it suitable for deployment across various agricultural settings, from small-scale farms to large commercial enterprises. The integration of IoT sensors, web-based interfaces, and data analytics algorithms empowers farmers to make data-driven decisions, optimize resource allocation, and reduce environmental impact. The system's ability to address soil deficiencies and provide customized fertilizer prescriptions aligns with sustainable agricultural practices and contributes to global food security.