In the field of Computer Science, Artificial Intelligence and Distributed Systems, fine-tuning large pre-trained multilingual language models on low-resource languages suffers from negative interference and high compute costs. This empirical investigation systematically examines Parameter-Efficient Modular Adapters for Cross-Lingual Transfer in Low-Resource Southeast Asian Languages through a multi-stage experimental methodology and rigorous quantitative analytical framework. Utilizing adapter-based parameter-efficient fine-tuning combined with contrastive sentence alignment across Vietnamese, Thai, and Indonesian, data were gathered across multiple operational cycles and validated against established international benchmarks. The statistical and computational results reveal that the adapter framework boosted cross-lingual NER F1-scores by 7.4 points over mBERT while updating only 2.8% of total model parameters. Comparative sensitivity analyses confirmed a statistically significant improvement (p < 0.01) over conventional baseline approaches, with heightened reproducibility and robust fault tolerance. These comprehensive findings provide actionable theoretical insights and practical implementation guidelines for NLP researchers and localization software engineers in multilingual emerging economies. Furthermore, the standardized protocols established in this study offer a valuable foundation for future cross-disciplinary investigations, policy formulation, and scalable technological deployment across global academic and industrial environments.