Digital transformation has introduced smart manufacturing, artificial intelligence, IoT, and advanced computerization to the pharmaceutical industry to drive Process Optimization. This plays a crucial role in the pharmaceutical industry as the complexity of manufacturing processes presents multidimensionality of product design, process development and product manufacturing data. While statistical techniques such as multivariate data analysis has made significant contribution to the pharmaceutical sector, its application can only be subjected to one process at a time in terms of providing support for quality-by-design based development and manufacturing of pharmaceuticals, limiting the enormous potential for automation. By leveraging machine learning, manufacturing processes can be streamlined to mitigate challenges associated with variability and complexity through predictive analysis of the large volume of data generated by PAT. This paper aims to provide a critical overview of how ML can be applied during various stages of the manufacturing process through a comprehensive analysis of existing literature from peer-reviewed journals, books, academic papers with illustrative examples applied in the context of pharmaceutical formulation development and related technologies as well as future trends. The study also aims to gain objective insights regarding the use of ML in pharmaceutical dosage manufacturing by exploring the opinions and perspectives of professionals actively involved in pharmaceutical manufacturing processes. With an estimated sample size of 90 participants, the study utilised an online survey-questionnaire that was administered to process managers, operators, industry experts, quality assurance and control officers to gather quantitative data in Ireland. An overall response rate of 69% was obtained and their opinion was evaluated in line with reviewed literature. The outcome of the study demonstrated the potential benefits that ML had to offer the pharmaceutical industry, the current applications, the limitations, and regulatory issues surrounding the adoption of ML in pharmaceutical manufacturing from both primary and secondary data sources.