Groundwater is a vital source of water in several regions across the world. Population growth and recurring extreme climate events present the need for an efficient groundwater management system to preserve the resource. The inaccessibility of groundwater level measurements in real-time and lack of understanding of the linkage between parameters that affect groundwater availability are among the challenges that hamper the efficient management of groundwater resources. Consequently, this study presents a prototype design and implementation of a groundwater level monitoring system based on the time lag between the onset of rainfall and groundwater level response. Rainfall was selected since it is the main parameter affecting groundwater availability and the source of input into the hydrogeological system. A nozzle rainfall simulator generated the rain over a sand tank (representing groundwater aquifer system) in a laboratory environment. The monitoring system, which was based on the Internet of Things (IoT), consisted of a tipping bucket rain gauge and an ultrasonic sensor (HC-SR04) for real-time measurements of rainfall and groundwater levels (GWLs), respectively. The data obtained was processed using an Arduino microcontroller and transmitted through a Wi-Fi network to a cloud-based ThingSpeak platform for online display in real-time. Data obtained from the monitoring system were analyzed for time lag selection between rainfall and GWLs. The findings of this study showed that real-time data on rainfall and GWLs was readily accessible, and the lag time and the interrelation between rainfall and groundwater levels were more accurately defined. Subsequently, the results will further be used to develop robust GWL predictive models, to enhance groundwater management through on-time decision-making.