TinyML is a revolutionary technique for utilizing AI in portable and low-powered devices. The need for more compact and concise systems grows by the day in order to provide smart services, particularly in the medical arena. This paper tries to fulfill these requirements by presenting the first-ever portable MWI-based TinyML brain stroke detection system with high accuracy. The head-imaging dataset, utilized here for the training of models, provides open-source data generated by our prototype head imaging system consisting of a low-cost vector network analyzer, single-board computer, rotating motor setup, and a Vivaldi antenna. The TinyML model is a compressed-size model of our proposed Deep Learning (DL) framework that obtains an accuracy of 93% on testing data with an F1-score of 0.929 deployed on the single-board computer. The compressed model obtained by pruning or quantization is not only small in size but also retains the above 90% accuracy of the DL model. This work reassures the possibility of successful deployment of TinyML based solutions in the microwave imaging systems for medical diagnostic applications in low-resource settings.