<p>Topology identification plays a key role in understanding and controlling complex dynamical networks (CDNs). However, existing topology identification methods generally require prior knowledge of system parameters or only guarantee asymptotic convergence, while also facing challenges including node parameter uncertainties, large-scale deployment constraints, nonlinear couplings, and multiple links. To address these issues, this paper proposes a unified finite-time framework for partial topology identification and parameter estimation in stochastic multiplex delayed complex networks (SMDCNs). By leveraging synchronization mechanisms, including complete synchronization and generalized synchronization, the proposed approaches are able to reconstruct the partial uncertain network topology and node parameters solely based on the state information in finite time, avoiding the requirement for prior knowledge or slow convergence of existing methods. Furthermore, it is proved that the state error and identification error converge to zero in finite time by employing finite time stability theory. Finally, the effectiveness of the proposed approaches is demonstrated through numerical experiments.</p>