This review explores the integration of machine learning (ML) and deep learning (DL) technologies in precision farming, highlighting the potential of web applications to improve agricultural decision-making through crop and fertilizer recommendations, disease detection, and aerial farm analysis. Precision farming technologies support sustainable agricultural practices by enabling real-time, data-driven insights for optimized resource use and yield enhancement. This review assesses various ML/DL models and their applications, including CNN-based disease detection and recommendation systems that utilize decision trees, neural networks, and satellite data analysis. Key challenges such as data quality, scalability, and security are discussed, along with future directions, including advancements in edge computing and federated learning. By identifying current limitations and prospective improvements, this paper aims to contribute to the development of comprehensive, scalable solutions that are accessible and effective for diverse farming environments.