A brain tumour is the result of the growth of abnormal brain cells, some of which have the potential to become malignant. Timely and accurate diagnosis of illness and implementation of treatment regimens improves patients’ quality of life and improves their life expectancy. Radiologists’ manual review of medical images is the conventional approach to diagnosing brain tumours, but it is laborious and error-prone. The article proposes a methodical approach to detect brain tumours. The suggested model is a near-ideal synthesis of nature-inspired and quantum-based algorithms, incorporating their more optimistic features. Using the quantum-based binary bat algorithm (q-BBA), the suggested model has been able to reduce dimensionality, or extraneous features. Machine learning classifiers such as SVM, Random Forest, Gaussian Naive Bayes and XGBoost were used to compute the optimality of features. After comparing QBBA’s performance with that of its conventional algorithms, it was found that, when applied to the same population, QBBA achieved better results. Having improved noise immunity and an average accuracy of 98.89%, QBBA emerges as a significant algorithm. Brain Tumour detection can be potential application of the suggested Quantum-based Binary Bat algorithm.