To evaluate the diagnostic accuracy of machine learning‐assisted magnetic resonance imaging (MRI) in detecting cognitive impairment among Parkinson’s disease (PD) patients through a systematic review and meta‐analysis. We systematically searched for studies that applied machine learning algorithms to MRI data for diagnosing PD with mild cognitive impairment (PD‐MCI). Data were extracted and synthesized to calculate pooled sensitivity, specificity, positive likelihood ratio (PLR) and negative diagnostic likelihood ratio (NLR), and diagnostic odds ratios (DOR). A bivariate random‐effects model and summary receiver operating characteristic (SROC) curves were employed for statistical analysis. The quality of studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS‐2) instrument. The publication bias was investigated through Deeks’ funnel plot. All statistical analyses were conducted using Stata 14.0. The pooled sensitivity and specificity for diagnosing PD‐MCI using machine learning‐assisted MRI were 0.82 (95% CI: 0.75–0.87) and 0.81 (95% CI: 0.73–0.87), respectively. The PLR was 4.28 (95% CI: 2.93–6.27), and the NLR was 0.23 (95% CI: 0.16–0.32), indicating a high diagnostic accuracy. The area under the curve (AUC) for the SROC was 0.85 (95% CI: 0.82–0.88). Quality assessment using the QUADAS‐2 tool showed a predominantly low risk of bias among the studies, and the Deeks’ funnel plot suggested no significant publication bias ( p = 0.30). In summary, the MRI combined with machine learning for diagnosing PD‐MCI achieved high accuracy with the pooled sensitivity of 82% and specificity of 81%.