This paper proposes a heavy–ball algorithm–based adaptive observer for the simultaneous estimation of states and constant parameters in a class of uncertain nonlinear systems subject to external disturbances. The estimator integrates a Luenberger–like observer for state estimation with a heavy–ball–inspired optimization algorithm to identify unknown constant parameters. The observer achieves exponential convergence of both state and parameter estimates. A Lyapunov–based analysis is employed to establish closed–loop stability under the standard persistence of excitation condition. The effectiveness of the proposed approach is validated through simulation studies, which demonstrate improved estimation accuracy compared to conventional adaptive observers.