This paper introduces a new data-driven bottom-up monitoring approach for distribution systems. In this approach, local estimations of the subsections into which the system is split are performed independently, thus leading to a scalable architecture. The monitoring approach is focused only on the estimation of voltage magnitude rather than the complete state of the system. This reduces the measurement requirements significantly, thus addressing economical and technical concerns for existing systems, while staying open to accommodating further incremental improvements in the available data and data quality. The estimation of each section is realized via an artificial neural network (ANN), for which a set of parameterizations is available to cope with different operating conditions. The estimation convergence is achieved even with relatively few measurements, although accuracy varies depending on the available measurements. At the Medium Voltage (MV) level, where reconfiguration is common, a configuration identification unit chooses the right ANN, the one trained for the actual network configuration. The estimation process is computationally simple and can be executed on low-cost hardware, as demonstrated in this paper by the implementation on a BeagleBone Black board. To demonstrate the concept, a prototype and a laboratory setup have been developed. The experimental test results are presented both for an Low Voltage distribution system and an MV distribution system.