Abstract: Forging is a critical manufacturing process used for producing key components for high-end equipment. However, there is currently a prominent contradiction between stringent quality requirements and limited process controllability. To overcome the limitations of traditional digital twin models in terms of mismatch and predictive capability, a new cyber–physical symbiosis paradigm is proposed. This paradigm establishes a tripartite integrated system consisting of the physical forging system, the digital forging model, and the cyber-physical interaction mechanism, enabling bidirectional driving and collaborative evolution between the physical entity and its virtual counterpart. Within this paradigm, Prognostics and health management (PHM) is positioned as the core value carrier and practical implementation pathway of cyber-physical symbiosis. Using a forging press as a case study, an integrated technical solution for anomaly detection and remaining useful life (RUL) prediction driven by cyber-physical symbiosis is constructed. A complete cyber–physical symbiosis-enabled intelligent health management system is developed using a forging press as the research object. In the perception layer, key parameters, including forming force, temperature fields, die temperatures, ram displacement, and velocity, were captured in real time via multisource sensor networks. These data streams were preprocessed through denoising, normalization, and cleansing to supply high-quality inputs for the virtual model. In the modeling layer, a high-fidelity surrogate model based on a physics-informed neural network (PINN) is designed. Learnable physical parameters, such as the coefficients of friction, leakage, and viscous damping, are embedded into the model, enabling physics-driven tracking of equipment degradation while maintaining prediction accuracy. Furthermore, a hybrid variational autoencoder–long short-term memory (VAE–LSTM) anomaly detection model incorporating physics-based losses was proposed. Combined with a dynamic threshold mechanism, this model enhances both the sensitivity to early faults and operational robustness. For RUL prediction, a physically interpretable health indicator was constructed using the degradation-related parameters identified through the PINN. A bidirectional long short-term memory–variational autoencoder (BiLSTM–VAE) time-series prediction framework was employed to achieve high-accuracy RUL estimation along with uncertainty quantification. In the application layer, a maintenance decision-support mechanism based on a multi-objective Markov decision process was established. Diagnostic alerts and RUL predictions are translated into actionable commands such as preventive maintenance, parameter adjustment, or emergency shutdown. Thus, a closed-loop feedback loop from the virtual space to the physical system was formed, ensuring continuous optimization. The proposed methodology was systematically validated using industrial forging press data. Experimental results demonstrated that the developed PINN surrogate model achieved low root-mean-square errors under both low- and high-pressure working conditions, exhibiting excellent predictive consistency and physical plausibility. The VAE–LSTM anomaly detection module was proven effective in identifying early -stage faults induced by abnormal furnace temperatures, whereas feature importance analysis was employed to provide interpretable evidence for identifying the root causes of anomalies. The RUL prediction model showed good agreement with the reference degradation trajectory generated by physical simulation, demonstrating its effectiveness under simulated degradation scenarios. Furthermore, the MDP-based maintenance strategy was observed to reduce the total lifecycle cost by approximately 65.14% compared with conventional scheduled maintenance, demonstrating its significant economic advantage and engineering applicability. This finding not only verifies the feasibility of the proposed symbiotic paradigm but also provides a new pathway for the intelligent health management of forging equipment.