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Brain injuries caused by strokes are one of the leading causes of disability worldwide. Current procedures require a specialised physician to analyse MRI images before diagnosing and deciding on the specific treatment. However, the procedure can be costly and time-consuming. Artificial intelligence techniques are becoming a game-changer for analysing MRI images. This work proposes an end-to-end approach in three stages: Pre-processing techniques for normalising the images to the standard MNI space, as well as inhomogeneities and bias corrections; lesion segmentation using a CNN network, trained for cerebrovascular accidents and feature extraction; and, classification for determining the vascular territory within which the lesion occurred. A CLCI-Net was used for stroke segmentation. Four Deep Learning (DL) and four Shallow Machine Learning (ML) network architectures were evaluated to assess the strokes' territory localisation. All models' architectures were designed, analysed, and compared based on their performance scores, reaching an accuracy of 84% with the DL models and 95% with the Shallow ML models. The proposed methodology may be helpful for rapid and accurate stroke assessment for an acute treatment to minimise patient complications. [ABSTRACT FROM AUTHOR]