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With the use of digital innovations including artificial intelligence (AI), the manufacturing business has been reshaped by the fourth industrial revolution. Yet, since rescheduling determinations are unpredictable and random mechanisms exist, controlling enhanced production scheduling still presents challenges. So, this study presents an innovative digital technologies-driven scheduling mechanism leveraging AI to tackle the difficulties of rescheduling in the aspect of production scheduling. A genetic-assisted beetle swarm optimization (GBSO) strategy is introduced for the flexible job shop problem (FJSP) with sequence-dependent configuration and constrained dual supplies. Then, rescheduling sequences are discovered using the support vector machine (SVM) approach. The Python environment is utilized for testing the suggested procedures, and the effectiveness of these approaches is examined in terms of scheduling latencies and the rate of rescheduling. This research demonstrated that the developed methodologies achieved the best performance in rescheduling frequency and scheduling latencies, thereby enhancing the manufacturing sector operations.