In the ever-evolving field of data storage and transmission, efficient text compression techniques play a critical role in optimizing performance and minimizing resource consumption. This paper presents a comprehensive comparative analysis of four prominent text compression algorithms: Run-Length Encoding (RLE), Lempel–Ziv-Welch (LZW), Huffman Coding, and a newly developed method termed Extended Huffman Coding. RLE is renowned for its simplicity and effectiveness in compressing repetitive sequences, while LZW offers a more sophisticated approach through dictionary-based compression. Huffman Coding utilizes variable-length codes to achieve optimal compression for symbol frequencies, making it widely adopted in various applications. Our proposed Extended Huffman Coding enhances traditional Huffman Coding by incorporating adaptive encoding mechanisms, thereby improving compression ratios for diverse text data sets. We evaluate the performance of these techniques based on several metrics, including compression ratio, speed of encoding/decoding, and memory efficiency, using a diverse set of text corpora. The results demonstrate that while traditional methods excel in specific scenarios, the Extended Huffman Coding consistently outperforms the others across a range of conditions, suggesting a promising avenue for future research and application in text compression. The purpose of this study is to offer insightful information to researchers and practitioners who are looking to put practical text compression techniques into practice.