The challenge of providing quality education to all, as outlined in SDG 4, is compounded by language barriers that hinder access to educational resources. This paper addresses this problem by exploring advancements in neural network architectures for machine translation. It covers key technologies such as Convolutional Neural Networks (CNNs), Gated Recurrent Units (GRUs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and the transformative Transformer model. Through a detailed literature review and algorithmic insights, the paper provides a comprehensive understanding of these methods. Examining case studies and experimental results, this paper highlights the impact of improved machine translation on educational accessibility for non-native speakers and multilingual regions. The advancements enable more accurate and relevant translations, enhancing learning outcomes. This research underscores the potential of machine translation to democratize education, showcasing it a powerful tool for achieving SDG 4 and fostering inclusive, equitable education globally.