<p>Precision agriculture has many challenges in resource-constrained rural environments, especially for maize disease monitoring in sub-Saharan Africa. This systematic review evaluates 62 latest research papers on intelligent wireless sensing architectures empowered by 5G/6G technology for real-time maize disease detection by means of multi-modal sensing, edge intelligence, UAV-assisted aerial sensing, NTNs, federated learning, etc. The process of synthesis shows that the application of AI-based edge computing with the aid of multi-modal fusion (RGB + VOC/ultrasound) helps to achieve pre-symptomatic and non-visual disease identification with high accuracies. The application of UAVs and NTNs also increases the coverage for remote maize fields. The application of federated learning frameworks helps to train models while considering the privacy of the rural sensors. The application of blockchain technology also increases the trust for IoT networks. The application of energy-efficient techniques helps to minimize the consumption for long-term operation. Despite all these advancements, some gaps persist, including a need for large-scale tropical field validations, heterogeneous protocol interoperation challenges, data heterogeneity for diverse agro-ecological zones, high deployment costs for smallholder farmers, and regulatory/spectrum challenges for NTN/UAVs. The review reveals that the hybrid edge cloud orchestration with the aid of AI-driven decision mechanisms, NTN/UAVs, and dynamic resource management is an emerging direction towards the realization of sustainable, secure, and scalable intelligent wireless sensing for maize disease monitoring. The above results provide a theoretical basis for future work towards empowering smallholder farmers in resource-constrained tropical environments and promoting sustainable precision agriculture for the betterment of SSA. </p>