Over the past decades growth of economy has been propelled by power networks worldwide. So, ascertaining a robust electric power network is paramount. The current situation involves grids with stressed operating points as a result of increasing load demand, which has led to considerable voltage limit deviations and elevated the possibility of voltage collapse. The necessity for voltage security evaluation has increased due to the importance of guaranteeing secure system functioning. As a result of the proliferation of smart equipment, an increasing amount of data is becoming available for system analysis. Machine learning and AI have given an evolutionary leap towards the development of efficient and robust technologies for swift, precise, and reliability in making decision under severe situations using voltage stability indices which acts as a crucial component in predicting the security status of the system, i.e., vicinity to voltage collapse or indicating the current operating stress point on the power network.