Niche modelling of species has become increasingly important in the context of accelerating climate change and anthropogenic impacts on the biosphere. One such tool for predicting the potential distribution of species is the maximum entropy method (MaxEnt). This method is particularly valuable when working with biodiversity data collected from herbaria and museum collections, as such data typically only contain information about where a species has been recorded, rather than where it is absent. It is precisely this feature of MaxEnt that makes it an indispensable tool for biodiversity research based on historical data. This allows for the reconstruction of historical species ranges, the detection of changes in their distribution, and the forecasting of future trends, namely the prediction of potential ranges, the assessment of the impact of climate change and anthropogenic pressure, and the development of effective biodiversity conservation strategies. This article provides a brief overview of the MaxEnt software’s operating principle, its capabilities, and limitations. In particular, it analyses the impact of data quality on modelling results and considers various approaches to assessing the importance of ecological factors for species distribution. One of the key issues discussed in the article is the problem of sampling bias. Sampling bias arises because data on the presence of species are often collected non-randomly and depend on the accessibility of the locality, the interests of researchers, and other factors. This can lead to distortions in modelling results. Various methods can be used to correct these biases, such as the bias grid method and the background points method. Another important aspect is the choice of the territory for the background sample. It should be taken into account that when using projections where cells have different areas, MaxEnt may give incorrect results. The article also emphasises the need for cautious interpretation of modelling results. Assessing niche models solely based on AUC (area under ROC curve) can be misleading, therefore, for a more reliable assessment of variable importance, it is worth supplementing it with permutation importance and the jackknife method. Examples of modelling for various groups, including mammals of the Ukrainian fauna, were considered.