Raman spectroscopy is a powerful technique used to analyse biological materials, where spectral markers such as proteins (1500–1700 cm⁻¹), carbohydrates (470–1200 cm⁻¹) and phosphate groups of DNA (980, 1080–1240 cm⁻¹) can be detected in a complex biological medium. Herein, Raman microspectroscopy imaging was used to investigate 90 brain tissue samples in order to differentiate meningioma Grade I and Grade II samples, which are the commonest types of brain tumour. Several classification algorithms using feature extraction and selection methods were tested, in which the best classification performances were achieved by principal component analysis-quadratic discriminant analysis (PCA-QDA) and successive projections algorithm-quadratic discriminant analysis (SPA-QDA), resulting in accuracies of 96.2%, sensitivities of 85.7% and specificities of 100% using both methods. A biochemical profiling in terms of spectral markers was investigated using the difference-between-mean (DBM) spectrum, PCA loadings, SPA-QDA selected wavenumbers, and the recovered imaging profiles after multivariate curve resolution alternating least squares (MCR-ALS), where the following wavenumbers were found to be associated with class differentiation: 850 cm⁻¹ (amino acids or polysaccharides), 1130 cm⁻¹ (phospholipid structural changes), the region between 1230–1360 cm⁻¹ (Amide III and CH₂ deformation), 1450 cm⁻¹ (CH₂ bending), and 1858 cm⁻¹ (CO stretching). These findings highlight the potential of Raman microspectroscopy imaging for determination of meningioma tumour grades.