This paper presents a novel traffic signal control methodology that combines Lyapunov Drift-Plus-Penalty (DPP) optimization with a shock-aware phase re-service mechanism and LSTM-based traffic forecasting to achieve both queue stability and delay minimization at urban intersections. The proposed controller dynamically reallocates green time in response to evolving queue lengths, particularly prioritizing oversaturated approaches to prevent congestion build-up and mitigate traffic shockwaves. LSTM networks are employed to capture real-time temporal patterns in traffic flow, enabling more responsive and data-driven decision-making. The framework is grounded in queueing theory and incorporates Kingman’s delay approximation to model system dynamics, while theoretical analysis ensures strong stability and provable near-optimal delay under admissible traffic demand. Extensive simulations were conducted across two scenarios. In the first, the proposed method was compared with traditional fixed-time control, achieving a 17.21% reduction in average waiting time, a 6.97% decrease in time loss, and a 2.35% reduction in average trip duration, with fewer teleport events and improved simulation efficiency. In the second scenario, compared to a baseline without shock-awareness, the proposed method reduced the average queue length by approximately 93.9% (from 263.54 to 16.09 vehicles) and the total queue delay by 93.9% (from 1054.17 to 64.38 vehicle \(\cdot \) slots), with 2,064 targeted shock re-allocations contributing to improved traffic stability. These results confirm the method’s effectiveness in both proactive congestion management and reactive traffic surge mitigation. This work contributes a theoretically grounded and practically viable adaptive signal control strategy, enhanced by real-time sequence modeling, with strong potential for scalable deployment in smart urban traffic systems.