This paper presents an enhanced tokenization framework specifically designed for the Hindi language, leveraging advanced machine learning techniques to improve the accuracy and efficiency of natural language processing (NLP) tasks. Tokenization, a vital preprocessing step, directly influences the performance of applications such as sentiment analysis, machine translation, and information retrieval. Hindi’s rich morphology, compound word structures, and diverse dialects present unique challenges for conventional tokenization approaches. To address these complexities, we propose a subword tokenization strategy that captures both character-level and word-level nuances. Our model is trained on a comprehensive Hindi text corpus using a neural network architecture composed of multiple transformer layers. The training process was optimized to achieve significant performance improvements, culminating in a training loss of 0.0186 and a validation loss of 0.0182 after 4750 iterations. Experimental results demonstrate that our model not only enhances tokenization accuracy but also effectively generates coherent Hindi text, showcasing its potential for real-world applications. The proposed framework contributes to the broader field of Hindi NLP by offering a scalable and adaptable solution that can be extended to other morphologically rich languages. Through this work, we aim to advance NLP technologies tailored to diverse linguistic communities, ultimately supporting more accurate and culturally aware language processing tools.