The paper examined a heavily stressed Nigeria 330kV network that operates closer to its thermal limits. The total forced outage recorded by Transmission Company of Nigeria (TCN) in the year 2020 was 53.4%, 42.43% in 2019 and 35.1 % in 2018. The inconvenience and economic cost of the occasioned forced outage on the public residence are enormous and unpleasant. With these statistics, this paper tends to evaluate the existing South-South 330kV grid for voltage stability improvement using Adaptive Neuro-Fuzzy. The network consisting of seven (7) generating station, sixteen (16) buses, and nineteen (19) transmission lines was modeled in NEPLAN 555 using Newton Raphson power flow algorithm to determine the operating condition of the existing network. Modal analysis and V-Q sensitivity was used to identify buses near voltage collapse. The result obtained from the base simulation shows that the 1st mode is the most critical in the network with the least eigenvalue of 35.5598 and the highest participating buses that showcased their proximity to voltage collapse in the mode are 12 (New Heaven, 0.4668) and 18 (Ugwaji, 0.4640). The P-V curve plot for base case simulation shows that at 710MW loading the operating point of Bus 12 (New Heaven) and 18(Ugwaji) are 93.925% and 93.956% respectively. The loadability can be increased by 1597.5MW and before a voltage collapse can be seen beyond which the system will not recover at 58.198% and 58.069% at 2307.5 MW loading. However, with ANFIS controlled SVC installed at Bus 12 and 18 respectively, the operating point increased to 98.437% and 98.508% at 710.0 MW loading and can be increased by 2840 MW before a voltage collapse can be seen beyond which the system will not recover, at 76.821% and 76.801% at 3550 MW loading. Therefore, increasing the loadability of buses by 1242.5MW with ANFIS controlled SVC.