Noise pollution in intensive care units (ICUs) is a growing concern in hospitals worldwide. Over the past few decades, most researchers have concentrated their attention on manually monitoring sound levels to assess the soundscape of ICUs, relying essentially on commercial sound level meters (SLMs). While these systems provide precise measurements, they are costly, making the regular and frequent deployment of multiple systems in many ICUs impractical, as well as being unable to conduct continuous measurements over extended periods. This, in turn, results in an inadequate amount of data being collected, leading to deficient analysis. This paper presents the development and implementation of a new and cost-effective Internet of Things (IoT)-based measurement system. In addition to serving as a compact and portable SLM by continuously measuring sound levels, it measures other environmental parameters, including temperature, humidity and light intensity, at a high resolution. A further feature of the developed system is its ability to automatically record a sound stream once the monitored sound level exceeds a preset threshold (dBthr), focusing on capturing the loudest and most significant sounds. The benefit is to avoid recording silent events, thereby saving digital memory. Collecting acoustic noise measurements over a set number of days in three ICUs, the system showcases satisfactory performance, bridging the accuracy gap between low-cost and commercially expensive alternatives.