With intensifying concerns over emissions, hybrid electric vehicles (HEVs) offer a practical bridge toward electrification by combining an internal combustion engine and an electric battery for improved efficiency and lower emissions. This study proposes a driving pattern recognition network-hybrid model predictive control (DPRN-HMPC) framework to improve the energy management of parallel hybrid electric vehicles (PHEVs) while reducing computational complexity. DPRN-HMPC leverages a pre-trained deep neural network classifier to identify the driver’s current driving pattern, which is then used to predict the wheel torque demand input for the model predictive control (MPC). This approach effectively predicts wheel torque demand—a stochastic variable—without relying on a computationally expensive stochastic model. Simulation results demonstrate that DPRN-HMPC improves average energy efficiency by 1.08% over linear deterministic MPC (LDMPC) across ten driving cycles. It maintains performance comparable to scenario-based hybrid MPC (scHMPC) while reducing computation time by 77.3%, ensuring feasibility within the 1,180-second limit of the New European Driving Cycle. Additionally, DPRN-HMPC achieves a 0.75% improvement in energy efficiency across five unseen driving cycles, demonstrating adaptability to new driving scenarios. These findings highlight the effectiveness of DPRN-HMPC in providing both practical and energy-efficient control solutions for PHEV energy management.