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The integration of remote sensing and machine learning in agriculture istransforming the industry by providing insights and predictions through dataanalysis. This combination leads to improved yield prediction and watermanagement, resulting in increased efficiency, better yields, and moresustainable agricultural practices. Achieving the United Nations' SustainableDevelopment Goals, especially "zero hunger," requires the investigation of cropyield and precipitation gaps, which can be accomplished through, the usage ofartificial intelligence (AI), machine learning (ML), remote sensing (RS), andthe internet of things (IoT). By integrating these technologies, a robustagricultural mobile or web application can be developed, providing farmers anddecision-makers with valuable information and tools for improving cropmanagement and increasing efficiency. Several studies have investigated thesenew technologies and their potential for diverse tasks such as crop monitoring,yield prediction, irrigation management, etc. Through a critical review, thispaper reviews relevant articles that have used RS, ML, cloud computing, and IoTin crop yield prediction. It reviews the current state-of-the-art in this fieldby critically evaluating different machine-learning approaches proposed in theliterature for crop yield prediction and water management. It provides insightsinto how these methods can improve decision-making in agricultural productionsystems. This work will serve as a compendium for those interested in yieldprediction in terms of primary literature but, most importantly, whatapproaches can be used for real-time and robust prediction.

Recent applications of machine learning, remote sensing, and iot approaches in yield prediction: a critical review