Over the past few years, the field of educational technology has experienced notable progress by incorporating advanced deep learning methods, namely, in the area of multilingual Natural Language Processing (NLP). This work examines the utilization of deep learning to improve multilingual natural language processing (NLP) in educational environments, with the goal of providing more efficient assistance to learners of different languages. We utilize advanced neural network topologies, including transformers and recurrent neural networks (RNNs), to create models that possess the ability to comprehend and produce human language in several languages. The research is centered around three primary domains: automated translation, speech recognition, and language generation. These models undergo training using large and diverse datasets in multiple languages to ensure strong performance and the capacity to adapt to different educational settings. The paper provides a thorough assessment of deep learning models by conducting a number of experiments in practical educational settings, including interactive language learning platforms and intelligent tutoring systems. The results demonstrate substantial enhancements in the precision and fluency of language processing tasks when compared to conventional NLP methods. Furthermore, the utilization of these models in educational contexts has shown improved learner involvement and expedited language learning, particularly in the field of early childhood education. The results emphasize the capacity of deep learning to revolutionize multilingual education through the provision of scalable, efficient, and highly effective tools for language learning.