Unmanned surface vessels operate in uncertain environments where unmodeled hydrodynamics and unmeasurable disturbances such as wind, waves, and currents pose significant challenges to control performance. To address these challenges, this paper presents a fractional integral terminal sliding mode controller for precise trajectory tracking and heading regulation. The design begins with a terminal sliding surface using a fractional‑order integral operator, guaranteeing finite‑time convergence of tracking errors while preserving signal smoothness. A wavelet neural network combined with adaptive control estimates unknown nonlinear dynamics and disturbances in real-time. An adaptive weight‑update mechanism derived from Lyapunov stability theory governs the neural network, ensuring boundedness of all closed‑loop signals and mitigating sliding mode oscillations. Simulation results on an unmanned surface vessel under time-varying environmental conditions demonstrate fast convergence, adaptivity, robustness, and high tracking precision.