In this work, an adaptive fractional-order sliding mode control approach for the location tracking control problem of a robot manipulator system is designed using neural networks. By designing a fractional-order sliding manifold, a fractional-order sliding mode-based control technique is designed to manage system uncertainties and external disruptions robustly. The designed controller uses a radial basis function neural network to reproduce the nonlinearity of the dynamical structure of the robot manipulator system. The controller’s adaptive bound part manages reconstruction error and calculates the upper bounds on the outside disruptions. Lyapunov and Barbalat’s conditions for stability are employed to assess the stability of the suggested control strategy. Consequently, the presented controller attains asymptotic error convergence and increases the controller’s efficacy. Finally, simulation investigations show that the suggested control technique is feasible.