With the rapid advancement of the Internet of Things (IoT), smart devices have become integral to various sectors, including healthcare, transportation, smart homes, and industrial systems. In agriculture, these technologies have driven a shift toward automation and data-driven decision-making through the adoption of smart sensing systems. However, most existing systems remain constrained by their reliance on centralized cloud infrastructure, resulting in latency, increased computational costs, and limited real-time capabilities. This work proposes an optimized microclimate sensor node architecture that incorporates edge computing abilities to enable real-time processing of sensor data and localized decision-making. These nodes are enhanced with Edge Artificial Intelligence to offer prediction analysis capacities at the edge. In this study, we focused on the prediction of missing sensor data using the intercorrelation between microclimate features, including temperature, humidity, CO₂, and light intensity. The study evaluates three supervised regression models, including Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), based on R2, RMSE, and MAE metrics. Experimental results demonstrate the superior performance of the RF model in learning the patterns between the microclimate parameters, achieving R2 scores of 98.88% in predicting temperature, 95.60% for humidity, 94.67% for CO₂, and 97.40% for light intensity. The results present the effectiveness of the model in predicting the missing parameters using the available ones. By enabling predictive inference at the edge, this approach optimizes the monitoring systems, reduces the dependency on the cloud servers, and contributes to sustainable agriculture, generally offering a scalable solution for resilient, intelligent, and real-time monitoring in precision agriculture.