Gaussian Mixed Model (GMM) has seen an increase in terms of usage especially to tackle the issue or problem of fraud activities in telecommunication industry. Like any other methods, GMM has its equal share of problems related to the maximum likelihood estimation and the determination of the number of components in GMM. In this paper we will highlight solutions to the said problems such as Expectation Maximization (EM) algorithm and the methods that are normally used to determine the number of components in GMM, which include the most recent research work done by the authors using Kernel method and Akaike Information Criteria (AIC); and the successful derivation of hypothesis testing in the determination of the number of components in GMM. The said derivation has never been attempted before due to the difficulty and complexity of GMM, as exemplified by the use of EM algorithm in solving its maximum likelihood estimation problem. The performance of the hypothesis testing, which is positive and promising despite using different percentage of overlapping; and the comparison of hypothesis testing to AIC, which produced conflicting results under certain conditions will also be highlighted