Recent advances in neuroimaging, genomics, and other technology driven data acquisition methods have greatly increased the complexity and volume of medical data. Traditional machine learning (ML) approaches are becoming increasingly difficult to apply in this context, particularly in neuroscience, where datasets are often high-dimensional but contain a limited number of samples because of the difficulty of collecting data from human participants. Although ML techniques are powerful tools for analyzing large datasets, they typically require substantial training sets containing balanced data and accurate labels. In real-world medical research, such data is rather rare. Consequently, small sample sizes can introduce bias in model performance estimates, thereby limiting the feasibility of predictive modeling. Nevertheless, such datasets are essential for identifying potential biomarkers and for conducting pilot or feasibility studies within the framework of personalized medicine. However, the limited sample size can lead to biased machine learning performance estimates, which makes it impossible to apply ML methods to predictive modeling. Therefore, artificial intelligence-based data mining tools are being developed to process large volumes of data and explore hidden features and correlations. This narrative review provides an overview of ML strategies tailored to neurological datasets with limited sample sizes, to better understand recent trends in this area and identify opportunities for future research. Particular attention is given to dimensionality reduction in complex data with few instances, as well as the integration of data mining and statistical learning techniques to improve the analysis and interpretation of small-scale but information-rich datasets.