This paper reviews the application of clustering techniques to functional magnetic resonance imaging (fMRI) time series data in the context of glioblastoma (GBM), a highly heterogeneous brain tumor. The review highlights the challenges in characterizing GBM and the potential of clustering algorithms to identify unique patterns within the dynamics of GBM. Clustering fMRI data has shown great potential in improving the differentiation between various subtypes of GBM, enhancing the monitoring of disease progression and response to treatment. The paper emphasizes the importance of integrating advanced data analysis techniques into neuroimaging and neuro-oncology research to develop personalized therapeutic strategies for GBM. The review also discusses the history of fMRI, its current applications, and future directions, including the integration of fMRI with other advanced imaging techniques and the study of neuroplasticity in GBM patients. Despite the limitations of fMRI, such as temporal and spatial resolution, the paper concludes that clustering analyses of fMRI time series can offer promising results in improving the discrimination between different GBM subtypes.This paper reviews the application of clustering techniques to functional magnetic resonance imaging (fMRI) time series data in the context of glioblastoma (GBM), a highly heterogeneous brain tumor. The review highlights the challenges in characterizing GBM and the potential of clustering algorithms to identify unique patterns within the dynamics of GBM. Clustering fMRI data has shown great potential in improving the differentiation between various subtypes of GBM, enhancing the monitoring of disease progression and response to treatment. The paper emphasizes the importance of integrating advanced data analysis techniques into neuroimaging and neuro-oncology research to develop personalized therapeutic strategies for GBM. The review also discusses the history of fMRI, its current applications, and future directions, including the integration of fMRI with other advanced imaging techniques and the study of neuroplasticity in GBM patients. Despite the limitations of fMRI, such as temporal and spatial resolution, the paper concludes that clustering analyses of fMRI time series can offer promising results in improving the discrimination between different GBM subtypes.