Published Paper Details:

Intelligent data processing, natural language processing, autonomous vehicles, and robotics are just a few of the many key applications and areas of research that benefit from the study of artificial intelligence. Machine learning (ML) refers to a set of procedures and techniques used to solve the issues of categorization, clustering, and prediction at the heart of AI. Artificial intelligence and machine learning's potential in the real world is bright. As a result, there is a lot of study being done in this field. But at the moment, AI's more intensive social use and its industrial uses are still not commonplace. Both AI itself (internal problems) and society as a whole (external problems) must be taken into account while attempting to tackle the difficulties of ubiquitous AI applications. Priorities for expanding the use of AI in industry and society can be determined after giving this issue careful thought. This article identifies and discusses some of the obstacles to using artificial intelligence in resource-based economies and societies. Publications in the field form the basis for the systematic application of AI&ML technology. Through this methodical approach, we are able to define our organisational, human resources, societal, and technological bounds. This review article lays out the research priorities in AI and ML that will allow us to extend the range of AI&ML's applications and overcome some of its limitations.