Probabilistic Neural Contextualization introduces a groundbreaking approach that bridges probabilistic reasoning with neural architectures to redefine contextual knowledge retrieval in computational models. By integrating dynamic contextualization mechanisms, it achieves enhanced adaptability and precision in managing uncertainty across diverse linguistic domains. The novel architecture incorporates advanced probabilistic inference layers, ensuring that outputs are consistently aligned with the complex demands of varied contextual scenarios. Comprehensive evaluations demonstrated marked improvements in key performance metrics, including perplexity and F1-score, validating the model's robustness and scalability. Experimental results further revealed its computational efficiency, maintaining performance consistency even under significant data variability and noise. Through seamless integration with open-source frameworks, the proposed methodology demonstrates its practical viability for large-scale applications, addressing critical challenges in knowledge-driven processing tasks. The study highlights a transformative advancement in contextual modeling, paving the way for more reliable, efficient, and intelligent computational systems capable of operating in complex and dynamic environments.