Aims: Pre-operative prediction of pleomorphic adenoma (PA) on magnetic resonance imaging (MRI) may allow a streamlined pathway for selection of patients to proceed to surgery without pre-operative biopsy. We aim to develop and evaluate an MRI-based diagnostic algorithm for parotid gland PA and explore its application in future clinical pathways to reduce reliance on preoperative biopsy. Methodology: A retrospective cohort study was performed in patients with histopathologically confirmed parotid tumours who underwent preoperative MRI and/or fine-needle aspiration cytology (FNAC) between 2015 and 2020. MRI examinations were reviewed by experienced head and neck radiologists using T1-weighted, T2-weighted, diffusion-weighted, and post-contrast sequences. MRI features associated with PA were identified and used to develop a structured diagnostic algorithm. Diagnostic performance of MRI and FNAC was compared, and the algorithm was retrospectively applied to assess its predictive accuracy. Results: MRI demonstrated diagnostic accuracy comparable to FNAC for predicting PA, with no statistically significant differences in sensitivity, specificity, or overall accuracy. Key imaging predictors included smooth or lobulated tumour morphology, well-defined margins, marked T2 hyperintensity, elevated apparent diffusion coefficient values, and absence of malignant features such as infiltrative growth, lymphadenopathy, or perineural spread. Application of the MRI algorithm demonstrated high specificity for identifying typical PA. Conclusion: This study proposes an MRI-based diagnostic algorithm to identify patients with characteristic PA who may be considered for extracapsular dissection surgery without biopsy. Future work includes training and validation of a new convolutional neural network utilising our MRI dataset and protocol; and commencing a prospective trial to validate the algorithm, incorporating all findings in assessing diagnostic accuracy, complications, patient-reported outcomes, and cost-effectiveness.