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The convergence of Artificial Intelligence (AI), the Internet of Things (IoT), and satellite remote sensing is transforming modern agriculture from an empirically driven labor practice into a highly data-centric discipline. While individual machine learning models for crop recommendation, disease detection, and yield forecasting have achieved high isolated accuracy metrics, current literature reveals a critical fragmentation in deployment: these modules rarely interact within a unified decision-support ecosystem. This review systematically analyzes the architectural requirements for an integrated Sense- Analyze-Act agricultural framework. It critically evaluates the efficacy of ensemble learning methods, specifically Random Forest classifiers, for soil-nutrient-to-crop matching; Convolutional Neural Networks (CNNs), like MobileNetV2, for edge- based phytopathological surveillance; and Long Short-Term Memory (LSTM) networks for temporal market price and soil moisture forecasting. Furthermore, the transformative role of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) in democratizing agronomic advisory services for smallholder farmers is systematically assessed. The review identifies key deployment barriers- specifically domain shift generalization, algorithmic bias attributable to non-representative training corpora, and the absence of robust multimodal sensor fusion architectures. Finally, it articulates a research roadmap emphasizing Federated Learning and autonomous unmanned aerial vehicle (UAV) surveillance to successfully bridge the gap between demonstrated algorithmic potential and smallholder operational reality.