The convergence of the Internet of Things (IoT) and Machine Learning in agriculture has drastically been changing traditional agricultural practices toward an intelligent, efficient future with the use of data-driven machinery. This paper suggests an integration approach towards predicting leaf disease in real-time and crop recommendation based on machine learning techniques (ML) with the help of internet-of-things devices (IoT). Leaf disease prediction is important to detect plant diseases at an early stage to prevent them, thus affecting agriculture’s productivity. It uses IoT sensors for environment sensing and machine learning algorithms for crop recommendation based on data provided by sensors in real time. The study will concern small-scale farms, which rely on resource management and in-time detection of infections to increase crop yield. IoT and ML-related techniques are combined in the proposed solution to provide a full-stack framework through which farmers can make informed decisions for improved agricultural productivity.