Twitter serves as a popular platform for sharing and connecting, but it is also a target for individuals who disseminate unwanted content, commonly known as spam. Recently, researchers have come up with different ways to find and stop this kind of spam on Twitter. This study explored multiple researches focusing on detecting spam on Twitter. Some relied on traditional machine learning methods, while others experimented with newer deep learning techniques. We did a careful review of four groups of questions researchers asked. We chose 48 studies out of 1080 articles from important journals and conferences. The chosen 48 studies were selected based on a range of spam detection techniques, with a focus on machine learning methods such as classification and clustering. We divided all the studies into five groups about finding Twitter spam. These five groups include publication years, evaluation parameters, evaluation tools, open issues and challenges, covered years. We also checked how these studies are spread out based on different things. We also talked about the problems with finding spam on Twitter. This includes how spammers keep changing tactics, not having enough good examples to learn from, and how it is hard to find spam in different languages. We aim to spread awareness about identifying and preventing spam across various domains. We also suggest new avenues for further research to take place. Our findings give a big picture of what’s going on in finding Twitter spam. They are helpful for people who work in this area like experts and researchers.