Ischemic Stroke and its subtypes detection in real time at an early stage is a complex task and requires advanced terminologies and tools to learn the statistical pattern from raw data and get generalize at various hospital patholabs and laboratories. In recent times, advanced supervised learning based algorithms are being used to develop laboratory equipments to resolve the issue of early detection. The paper presents an analytical and diagnostic suggestion by employing variants of supervised learning—hyperparameter tuned-machine learning algorithms, ensemble learning algorithms, and a dependency-based neural network architecture LSTM on patient-level data of patients with ischemic stroke. The objective of research is to get the most effective algorithms for the timely diagnosis of ischemic stroke and its varieties. Algorithms of ensemble learning (Random forest, Xgboost, and LightGBM) showed accuracy more than 80%, for both ischemic stroke and its subtypes confirming the overall superiority of the chosen approach in contrast to the comparisons made. The gated RNN architecture—LSTM model also showed an accuracy of 80 percent, but with a less recall value which shows a trade-off with sensitivity and is highly prone to overfitting and performs well in training data only. This research provides a glimpse for any clinician or healthcare entrepreneur at the applicability of ensemble learning algorithms for early-stage stroke detection while building realistic, efficient, and reliable models for practising clinicians on how to implement such an approach within their practice.