On the telecommunications front, 5G is the fifth-generation technology standard for broadband cellular networks, which is a replacement for the 4G networks used by most current phones. Hundreds of businesses, organizations, and governments suffer from cyberattacks that compromise sensitive information in which 5G is one of them. Those breaches of the data would not have occurred if there is a way to detect strange behaviors in a 5G network, and this is what this paper presenting. Network Anomaly Detection (NAD) in 5G is a way to observe the network constantly to detect any unusual behavior. However, it is not that straightforward and rather a complex process due to huge, continuous, and stochastic network traffic patterns. In the literature, several approaches and methods have been employed for anomaly detection as well as prediction. This paper illustrates state-of-the-art method to proposed achieve the NAD. For instance, pattern based, machine learning based, ensemble learning based, user intention based, and some integrated methods have been surveyed and analyzed. KNN and K-prototype algorithm were tested together on the dataset and compared with integrated approach. The integrated approach outperformed with respect to the KNN and K-prototype methods. As a conclusion, forecasting of analyst detection of cyber events is presented as a final method for future anomaly prediction.