Predicting pedestrian trajectories in crowded and dynamic scenes is crucial for risk aware decision making in safety-critical applications. In practice, it remains challenging because trajectory forecasting must jointly model inter-pedestrian interactions to avoid socially/physically implausible futures and capture the inherent multimodality and uncertainty of human motion. To address these challenges, we propose Synergistic Diffusion-adversarial Method (SyDiM), a pioneering generative framework that seamlessly integrates diffusion-based probabilistic modeling with adversarial training strategy in pedestrian trajectory forecasting. Our method leverages diffusion to preserve multimodality and couples it with adversarial social constraints to ensure physically and socially valid interactions. Specifically, we design a Contextual Refinement Module (CRM) within the generator that performs context alignment to capture spatio-temporal dynamics and social context, effectively encoding pedestrian motion patterns while refining group-level interactions in crowded scenes. To enforce social feasibility without sacrificing the multimodality afforded by diffusion, we design an Entity-aware Spatio-temporal Trajectory Discriminator (ESTD). By integrating pedestrian-specific embeddings, it is designed as a feasibility evaluator that uses spatio-temporal attention to identify socially implausible interactions. It then guides the diffusion generator by reweighting its trajectory proposals, effectively suppressing unrealistic trajectories while preserving diversity. To the best of our knowledge, ESTD is the first interaction-conditioned feasibility evaluator tailored for diffusion-based multi-proposal trajectory forecasting. It produces a feasibility score for each predicted trajectory, which is then used to reweight samples during training. Extensive experiments on NBA SportVU, ETH-UCY, and Stanford Drone Datasets demonstrate that our SyDiM achieves consistent performance improvements across all benchmarks. Code will be available at https://github.com/xdclby/SyDiM .