Abstract: Ferroelectric thin films represent promising candidates for artificial synapses in neuromorphic computing. However, electric-field cycling can induce the formation of defect clusters, resulting in electrical fatigue. In this study, phase-field simulations were employed to investigate the influence of defect clusters on ferroelectric BaTiO₃. Using 3D Voronoi tessellation, randomly selected regions were transformed into a charged paraelectric phase to simulate defect-cluster formation, enabling an accurate reproduction of fatigue behavior. An inhomogeneous-field framework was utilized to analyze statistical polarization-switching dynamics, which directly impacts the performance of neuromorphic devices. Defect clusters were found to reduce the switching energy barrier and extend the domain-wall depinning time. Spike-timing-dependent plasticity (STDP) and long-term potentiation/depression (LTP/LTD) were evaluated. A robust synaptic response was observed at short delay times, whereas longer delays weakened the correlation when 10% defect clusters were present. Moderate fractions of defect clusters allow for gradual yet asymmetric and nonlinear weight updates. Vector-matrix multiplication tests demonstrated that BaTiO₃ films with 10% defect clusters achieve the highest inference accuracy (94.84%). This study provides practical guidance for optimizing ferroelectric thin films for neuromorphic computing applications.