The stringent requirements demanded by grid codes worldwide have sparked immense research activity in applying new and enhanced methods in grid-connected wind generators to improve their Low Voltage Ride Through (LVRT) capability. Rapidly increasing wind energy penetration into the conventional grid has made it necessary to implement preventive control techniques to successfully detect voltage sags and compensate them before the system is disconnected from the grid. The various techniques available for LVRT enhancement are classified into control techniques and hardware technologies. Artificial intelligence-based algorithms help to enhance and accelerate the performance of conventional control techniques like PI controllers, hysteresis controllers, and sliding mode controllers. Machine learning, a subset of Artificial Intelligence (AI) can be successfully applied for power system fault detection and outage management. The role of various AI based optimization techniques like Genetic Algorithms, Artificial Neural Networks, and ANFIS Systems are compared. Also, the ability of different machine learning methods including supervised machine learning techniques like linear regression, decision trees, and reinforcement learning algorithms in augmenting the LVRT capability of various grid-integrated wind turbines were reviewed in this work. The application of various AI algorithms in improving the performance of controllers in FACTS devices during voltage sags are also reviewed.