In this work, investigate the feasibility of employing a Hidden Markov model for Image Click Point Authentication. The focus of this research is on finding a way to generate a password from just four select-able areas of an image. Users must verify their identities before gaining entry to protected locations. Password-based authentication is by far the most widespread method. Attackers can easily figure out a user's text-based password by using social E-commerce tactics. These days, the internet is used for everything. Online shopping's popularity has exploded in recent years. The term “e-commerce” refers to the practice of conducting business transactions (such as retail shopping) electronically over the Internet. Credit cards have quickly become the preferred mode of payment for both online and in-person purchases. As the use of credit cards for internet purchases grows more commonplace, so does credit card theft. In this study, we use a Hidden Markov Model to simulate the steps of a credit card transaction (HMM). A user's identity can be checked by having them click on specific spots within a moving image on the screen at specific intervals. With graphical authentication, the user creates a sequence string by clicking on different areas of an image. After the algorithm has decided that an attack is very likely, the compromised account will be immediately disabled.