Artificial intelligence (AI) and machine learning (ML) have the potential to improve productivity, sustainability, and industrial efficiency. However, there are several obstacles to overcome before applying AI to manufacturing. These include issues with infrastructure, human resources, security, trust, and data administration and acquisition. For instance, obtaining the data required to train AI models for uncommon occurrences or big datasets that require labelling may prove to be challenging or expensive. Artificial intelligence (AI) model integration into industrial control systems might raise additional security issues. Furthermore, some industry participants can be reluctant to employ AI because they don't trust it or don't know how it works. Despite these challenges, artificial intelligence (AI) holds significant potential to improve manufacturing, particularly in areas like process optimisation, quality assurance, and predictive maintenance. Enhancing comprehension of the technologies available for manufacturing problem-solving, identifying areas where further research can yield industry-transforming returns, and providing decision-support for the prioritisation and selection of appropriate AI/ML technologies are the goals of this review. The study centres on the most recent advancements, difficulties, and potential paths in AI/ML that are pertinent to business. Preliminary research indicates that AI/ML has the potential to dramatically lower costs and boost productivity in the manufacturing industry, especially when combined with the ability to collect enormous amounts of data from production systems.