This study delivers a rigorous scientific analysis of the influence of Artificial Intelligence (AI) applications on Total Quality Management (TQM) and the advancement of a continuous improvement culture in industrial organizations, set against the backdrop of the accelerating transformation toward the Fourth Industrial Revolution (Industry 4.0) and the paradigm of Quality 4.0.

The research adopts a descriptive-analytical methodology, employing a systematic and critical review of contemporary literature (2015–2025), including both international and Arab studies. It examines the interplay between AI technologies—such as machine learning, predictive analytics, expert systems, and computer vision—and the core pillars of TQM, which encompass leadership, customer orientation, continuous improvement, and operational innovation.

The findings demonstrate that AI enhances the precision of quality control processes, the anticipation of operational challenges, the quality of decision-making, and the efficiency of industrial operations. Additionally, the study shows that intelligent, self-learning systems can generate innovative solutions derived from historical data, thereby strengthening continuous improvement mechanisms and bolstering organizational agility and competitiveness in dynamic environments.

The paper culminates in the development of an integrated conceptual framework that elucidates the relationships among AI dimensions, TQM components, and key performance indicators, while also proposing a forward-looking research agenda for empirical validation. The practical contribution lies in presenting a detailed implementation roadmap for the adoption of AI within industrial quality systems, aimed at achieving operational excellence and institutional sustainability.