Diabetes prevalence is high in Kenya's coastal districts, particularly among vulnerable populations such as older adults and those with limited access to healthcare. A systematic review approach was employed, evaluating existing studies on IoT-based diabetic care systems. Data were analysed using meta-regression models to assess the impact of device features on patient outcomes. The analysis revealed a significant improvement in glycemic control (HbA1c reduction by 5% with devices featuring continuous glucose monitoring) among high-risk populations, though variability exists across studies regarding efficacy and cost-effectiveness. IoT-based health monitoring devices show promise for personalized diabetes management in high-risk Kenyan coastal communities, warranting further research to optimise device design and implementation strategies. Future studies should focus on developing user-friendly devices and exploring the economic impact of IoT solutions in underserved regions. IoT, Diabetes Management, Personalized Health Monitoring, Meta-Analysis, High-Risk Populations Treatment effect was estimated with $\text{logit}(p_i)=\beta_0+\beta^\top X_i$, and uncertainty reported using confidence-interval based inference.