Due to the availability of vast amounts of data related to various diseases, a heterogeneous environment, structured and unstructured data, and people's awareness of their individual health status, healthcare is an emerging field of study. Smart devices, fitness bands, sensors, and healthcare apps all play a vital role in the field of healthcare, as do technological advancements. The invention of these devices advanced medical care to the next level. Consequently, everyone is concerned about their health status and potential outcomes. Therefore, the most important aspect of the healthcare industry is the accurate analysis of health data and healthcare services. On the other hand, machine learning is a well-known and extensively utilised method for analysing and predicting healthcare data. The purpose of machine learning is to make accurate decisions and diagnose diseases earlier. Additionally, the primary objective of this work is to address accurate and timely disease diagnosis. This study examines the stroke dataset for more rapid and precise diagnosis. Numerous ML techniques have been reported in the literature, but accuracy is the primary concern, particularly with healthcare datasets.