Cross-lingual transfer learning is incredibly promising for facilitating knowledge transfer between languages, particularly for low-resource languages that lack annotated data. However, many current methods are inefficient in terms of adaptation, have poor generalizability, and often fail to incorporate external real-world or linguistic knowledge. This paper introduces a Unified Framework for Efficient CrossLingual Transfer Learning Across Low-Resource Languages using Knowledge-Augmented Multilingual Models. The approach integrates structured and unstructured knowledge sources, such as multilingual knowledge graphs, lexical resources, and cross-lingual embeddings, into pre-trained multilingual language models (like XLM-R and mT5) through adapter-based fine-tuning and prompt-guided alignment. This creates a task-agnostic transfer pipeline that jointly optimizes for semantic alignment, knowledge consistency, and lowresource adaptability across multiple NLP tasks, including machine translation, named entity recognition, and question answering. Experimental results on 25 typologically diverse languages, including some with fewer than 10,000 training examples, demonstrate that the framework achieves state-of the-art performance, significantly surpassing current multilingual baselines in zero-shot and few-shot regimes. Furthermore, ablations reveal the critical contribution of knowledge integration to improving contextual disambiguation and representation fidelity for low-resource languages, providing a foundation for creating scalable, knowledge-driven multilingual systems that help close the digital linguistic divide.