Generative models have been immersed in various artificial intelligence applications. This field of research is also known as generative artificial intelligence (GenAI). Any form of data (e.g., videos, images, audio, text, and time-series data) can be generated, providing additional and useful data to enhance the performance of machine learning models. GenAI features the ability to address various issues, such as rare event collection, high cost of data labelling, bias mitigation, privacy, and data scarcity. Wireless communications have become a vital part of today’s digital era, offering flexibility, accessibility, ease of maintenance, and cost-effectiveness. Smartphones, tablets, notebooks, and smartwatches are common tools that rely on wireless communications. In this research, we conducted a literature review to analyze the 1180 latest relevant works (published in 2015–2024) employing GenAI for wireless communications. The algorithms, applications, and results were discussed and compared. The analysis focused on fundamental characteristics such as the yearly publication count, the various subject areas of these publications, the leading ten journals and conferences, as well as a visual representation of keywords in a word cloud. This was succeeded by a thorough examination of the ten most frequently cited publications. Additionally, two significant open challenges were highlighted, along with recommendations for possible future research avenues.