<p>As intelligent algorithms rapidly develop, the traditional automation level of furniture component production can no longer meet the practical needs. To address this issue, different job time models are constructed based on the characteristics of manual and machine operations. At the same time, the bottleneck of the One Piece Flow Production Line (OPF) is identified using production line balance theory, and the production line is planned and optimized. Finally, a digital twin automatic production model for furniture components based on job prediction and production line optimization is created. The findings denoted that the average deviations between the predicted and measured values of the three-layer artificial neural network model on the training and testing sets were 1.301%, 1.492%, and 4.044%, respectively, which were better than the comparison models. Optimizing the arrangement of drill bits could significantly improve production efficiency. Directly solving reduced the number of drilling trips and operation time, resulting in better grouping results. Compared to the actual situation, the amount of drilling trips on the front and back sides was reduced by 26.70% and 10.68%, respectively, and the average operation time was reduced by 6.80%. After deploying the digital twin system, the utilization rate of drill bits, daily energy output, and abnormal response efficiency increased by 31%, 36%, and 88%, respectively. The bottleneck process time, unit energy consumption, and defect rate decreased by 42%, 33%, and 0.9%, respectively. The findings denote that the research method can effectively raise the production efficiency of furniture components, reduce energy consumption, and enhance flexible production capacity, providing a reusable technological path for the digital transformation of the furniture manufacturing industry and promoting the industry’s development towards intelligence.</p>