Humidity, salt content, and migration in building materials lead to weathering and are a common challenge. To understand damage phenomena and select the right conservation treatments, knowledge on both the amount and distribution of moisture and salt load in the masonry is crucial. It was shown that commercial portable devices addressing moisture are often limited by the mutual interference of these values. This can be improved by exploiting broadband radar reflectometry for the quantification of humidity in historic masonry. Due to the above-mentioned limitations, today’s gold standard for evaluating the moisture content in historic buildings is still conducted by taking drilling samples with a subsequent evaluation in a specially designed laboratory, the so-called Darr method. In this paper, a new broadband frequency approach in the range between 0.4 and 6 GHz with improved artificial-intelligence data analysis makes sure to optimize the reflected signal, simplify the evaluation of the generated data, and minimise the effects of variables such as salt contamination that influence the permittivity. In this way, the amount of water could be determined independently from the salt content in the material and an estimate of the salt load. With new machine learning algorithms, the analysis of the permittivity is improved and can be made accessible for everyday use on building sites with minimal intervention by the user. These algorithms were trained with generated data from different drying studies on single building bricks from the masonries. The findings from the laboratory studies were then validated and evaluated on real historic buildings at real construction sites. Thus, the paper shows a spatially resolved and salt-independent measurement system for determining building moisture.