In the current information-rich era, efficient retrieval and classification of text-based documents have become crucial tasks. With the exponential growth of digital content, the ability to retrieve the most relevant and appropriate documents has become a pressing concern. Effective document retrieval not only saves time and effort but also contributes to enhanced knowledge discovery and decision-making processes. To address these challenges, various text-based classification techniques have been developed and implemented. This paper aims to provide a comprehensive review and analysis of text-based classification techniques. The objectives include evaluating existing methods, identifying their strengths and limitations, and suggesting potential avenues for future research. The paper will analyze various algorithms, feature extraction techniques, and evaluation metrics employed in text-based classification. Additionally, it investigated the impact of different factors, such as document size, language, and domain specificity, on classification performance.