Natural language processing (NLP) has increased the interest in genetic algorithm (GA) due to their skills in solving complex optimization problems with extensive research on the use of genetic algorithms in NLP projects has been presented in this paper. First, we present the basic concepts behind genetic algorithms and their relevance to natural language processing. Then, we explore various applications of natural language processing (NLP) that use genetic algorithms, including text classification, sentiment analysis, machine translation, summarization, and question-answering systems. We examine the advantages and disadvantages of genetic algorithm applications in natural language processing by comparing their performance with traditional and modern approaches and discuss the factors influencing their effectiveness. Furthermore, we explore recent advancements, modifications, and hybridizations of Genetic Algorithms tailored to NLP tasks. Finally, we discuss the challenges and future directions in leveraging Genetic Algorithms for enhancing NLP technologies.