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The clinical monitoring of the central nervous system (CNS) tumors is challenging due to the limited availability of predictive imaging tools and the complexity of tumor progression. Meningiomas, which account for 35 % of CNS tumors, require precise growth assessment to allow precise clinical decision-making. Current manual assessment protocols rely on recurrent imaging using magnetic resonance imaging (MRI) and computed tomography (CT) to monitor tumor growth and the evolution of intrinsic features over time. However, accurately predicting tumor growth risks and rates has been a difficult challenge utilizing contemporaneous technology.In this study, we introduce a novel application of machine learning (ML) for predicting meningioma growth risks - categorizing tumors as growing, stable, or shrinking - and further estimating their volumetric growth rate using neurosurgeon-assessed clinical features derived from only one single imaging timepoint. We used 12 features including calcification, cerebrospinal fluid (CSF) plane, oedema, location, T2 intensity, regularity, sex, ethnicity, and age at diagnosis, derived from MRI and CT scans of 336 patients treated at Auckland City Hospital. Importantly, no volumetric data were included amongst the features to ensure non-biased model reliability. Our results demonstrate that machine learning can accurately predict meningioma growth risk and volumetric growth rates, achieving remarkably high accuracies exceeding 99 %. Among the tested ML models, k-nearest neighbors (KNN) consistently outperformed the others in both prediction tasks under 5-fold and 10-fold cross-validation schemes.Clinical relevance-This study establishes a paradigm for using ML to predict meningioma growth risk and progression, which provides an opportunity for improved patient-specific tumor monitoring and potentially for early intervention.