The agricultural industry faces major problems as a result of climate change, which necessitates the development of novel solutions for climate-resilient agriculture. This literature analysis focuses on more current research publications in the subject of agriculture to investigate how technologies such as the Internet of Things (IoT) and Machine Learning (ML) might be integrated to improve crop production. The Internet of Things and machine learning have a wide range of applications in agriculture, including weather monitoring, precision agriculture, crop health monitoring, control of pests and diseases, and resource allocation. These technologies facilitate the making of informed decisions, optimise the utilisation of available resources, and promote risk reduction. They provide a contribution to enhanced agricultural yields, greater efficiency in the use of resources, and increased environmental sustainability. However, there are issues that need to be resolved, such as data privacy and security, data integration, and accessibility for small-scale farmers. In the future, research should concentrate on adapting solutions to specific regional circumstances, ensuring that they are resilient to the effects of climate change, and encouraging the widespread use of IoT and ML technologies in agricultural production. The combination of the Internet of Things and machine learning offers the potential to make agriculture more resilient and environmentally friendly in the face of changing climatic patterns.