<p>Datastreams serve as the fundamental conduits of information in contemporary society, playing a pivotal role in various aspects of our daily lives. This paper delves into the utilization of datastreams, elucidates the challenges encountered during their application, and presents potential solutions to overcome these challenges. Different approaches to handle datastreams are discussed, encompassing diverse methodologies. Some methods emphasize the employment of mathematical and statistical techniques for comprehending and analyzing data, while others capitalize on machine, deep, and continual learning to extract meaningful patterns. Furthermore, the paper explores the emergence of quantum machines that leverage the principles of quantum physics to tackle datastream related tasks. The advent of quantum machines holds promising prospects for revolutionizing data handling mechanisms in the future. In addition to data analysis, the paper delves into the platforms and systems employed to effectively process and organize datastreams. These platforms act as indispensable tools, facilitating efficient management and manipulation of the copious volumes of data within datastreams. Measurement of datastream performance is another key aspect addressed in the paper, wherein several metrics are examined. These metrics provide valuable insights into the efficacy of datastream methodologies. An intriguing subject discussed is eXplainable AI (XAI) datastreams, which offers a means to comprehend the decision-making processes of complex algorithms, such as neural networks. Lastly, the paper highlights the persisting challenges encountered in working with datastreams and outlines potential future directions in the field. These insights shed light on the evolving landscape of datastream utilization and anticipate the exciting possibilities that lie ahead.</p>