<p>This study addresses the predefined-time leader–following consensus problem for stochastic nonlinear multi-agent systems and develops a rigorous adaptive control framework with guaranteed performance. Compared with conventional finite-time and fixed-time control schemes, the proposed strategy enables the convergence time to be explicitly prescribed a priori and to be independent of initial conditions. By constructing an appropriate stochastic Lyapunov function and employing Itô calculus, sufficient conditions are derived to ensure predefined-time stochastic consensus in the mean-square sense. Unknown nonlinear dynamics and stochastic disturbances are handled by incorporating fuzzy logic systems into the adaptive control design to approximate uncertain nonlinear functions and formulating adaptive laws to ensure parameter boundedness and compensation accuracy. Theoretical analysis establishes the boundedness of all closed-loop signals and proves convergence within the assigned time. Numerical simulations are provided to validate the effectiveness, robustness, and theoretical findings of the proposed method, demonstrating its suitability for complex and uncertain stochastic multi-agent systems.</p>