Arabic Sign Language (ArSL) presents formidable communication barriers for 17–23 million deaf individuals across 460 million Arabic speakers. We introduce a neural-symbolic framework addressing dialectal variation through spatiotemporal constraint injection, embedding grammatical rules as optimizable symbolic loss terms. Our multi-agent coordination system employs symbolic validators that inject dialect-specific rules with physics-informed motion synthesis to generate culturally authentic gestures. The framework achieves 40% higher accuracy than Mahmoud et al. [1], with 95% gesture fidelity (F1@0.5 IoU) and 85 ms median latency on consumer hardware. Cultural authenticity scored 4.6/5 by native signers across 15 dialects. Real-world deployments in educational and healthcare settings elevated communication success rates from 45% to 83%. The most persistent errors (3%) stem from Levantine trilateral root conflations—exemplified by ṣaḥīfa ( , ”clear newspaper”) versus ṣuḥuf ( , ”multiple newspapers”)—demonstrating the model’s capacity for morphological decomposition while maintaining cultural coherence.