The widening gap between energy generation and demand on a global scale, coupled with the imperative to reduce emissions, has necessitated the development of largescale sustainable energy solutions. Among the various renewable energy options, Wind Power stands out as a viable source capable of generating substantial amounts of electricity. However, the unpredictable nature of wind availability and its fluctuations pose challenges for grid operators in effectively harnessing and distributing the generated wind power. This issue becomes more pronounced when transmitting wind power through local grids to distant load centers. Voltage instability at local buses emerges as a significant concern in wind-integrated power systems. To address these challenges, dynamic compensation at multiple locations has proven to be an effective solution. Various alternative approach to controlling the firing of Static Var Compensators (SVCs) connected to the network is proposed in the present work. The traditional method, which relies on a classical control approach, is computationally intensive and time-consuming. To overcome this limitation, we propose the utilization of a trained Neural Network for simultaneous control of the firing angles of all SVCs, accommodating various system conditions such as change in load and wind generation fluctuations. Porposed method has been evaluated on both a modified IEEE-30 bus system and a 28-bus Indian system.