Hydrothermal deposits are key indicators of the presence of geothermal energy and minerals of industrial interest. This study proposes an integrated Fuzzy-Boolean approach adapted to arid areas with low data density to produce a new model of hydrothermal exploration in a dry environment with low and dispersed vegetation cover, using Central Africa as a test case. To achieve this goal, hydrothermal alteration model obtained from ASTER data were combined and cross-validated with index mining obtained from geological and field data. Potential hydrothermal zones were identified, located, interpreted and spatially distributed using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data, natural gamma-ray spectrometry, morphological and geological data. The use of Fuzzy operators such as the algebraic sum and product with the gamma operator allowed a flexible combination of thematic layers, by managing exclusion and gradation relationships. Fuzzy logic alone has made it possible to reproduce the proposed model using explicit rules, unlike some machine learning approaches which require large training and validation datasets. Despite incomplete, imprecise or scattered information in the arid region, where geological, geomorphological and spectrometric contrasts were not always clear-cut, it simultaneously integrated these elements and offered better management of uncertainty and gradual transitions between classes (favourable and unfavourable zones). Combined with the index overlay method (IOM), it created a flexible way of visualising, in linear shapes and halos, the vast corridors of possible hydrothermal deposits. This modelling provides an important clue for identifying geothermal systems in the region, resulting in the formation of hydrothermal deposits in places where the circulation of hot fluids (associated with active tectonic structures and plutonic intrusions) favours alteration of the host rocks. Using innovative modelling approaches, the study highlights the potential of Boolean and Fuzzy logics as complementary tools in the hydrothermal field, as well as their adaptation to predictive mapping in arid environments where sparse vegetation, low spectrometric contrasts and sparse field data make the application of statistical techniques less reliable.