This paper presents a systematic analysis of lemmatization techniques powered by artificial intelligence (AI). Lemmatization, the process of reducing
words to their base or root form, is crucial in natural language processing (NLP) for improving the accuracy and efficiency of text analysis tasks. Traditional lemmatization methods often rely on predefined rules and linguistic resources, which can be limited and inflexible. In contrast, AI-driven approaches, particularly those utilizing machine learning (ML) and deep learning (DL) algorithms, offer adaptive and context-aware solutions. This analysis reviews the evolution of lemmatization techniques from rule-based systems to advanced AI models, including neural networks (NN) and transformer-based architectures. It evaluates the performance of these AI-based methods in terms of accuracy, computational efficiency, and adaptability to diverse languages and domains. Additionally, by providing a comprehensive comparison and highlighting current trends and future directions, this study aims to offer insights into the effectiveness of AI-driven lemmatization and its potential to advance the field of NLP.