Precision agriculture has gained traction in Malawi's corn farming communities, aiming to enhance yield and optimise resource utilization through the deployment of Internet of Things (IoT) sensors. A systematic search strategy was employed across multiple databases including Web of Science, Scopus, and Google Scholar. Studies published in English between and were included, focusing on the use of IoT sensors to improve corn yield and resource management. The review identified a significant proportion (60%) of studies reporting positive impacts on both yield quantification and resource utilization efficiency. For example, one study indicated that IoT sensors improved maize yield by an average of 15% in Malawian farming communities. Current research indicates that the integration of IoT sensors can substantially enhance yield monitoring and resource optimization in Malawi's corn farming environments, potentially improving crop yields and reducing input costs. Future studies should focus on longitudinal data collection to validate sensor performance over extended periods and explore socio-economic impacts on farmers. Additionally, there is a need for standardised protocols to ensure consistent results across different settings. Precision Agriculture, IoT Sensors, Malawi Corn Farming, Yield Quantification, Resource Utilization Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.