Background and Objectives: Differentiation between cystic pituitary adenoma and Rathke's cleft cyst (RCC) is clinically important because these lesions require different treatment strategies. This study aimed to develop and internally validate an MRI-based multivariable diagnostic prediction model to differentiate cystic pituitary adenoma from Rathke's cleft cyst before treatment. Materials and Methods: A retrospective analysis was performed on 56 adult patients (27 cystic pituitary adenomas and 29 RCCs) who underwent pituitary MRI between 2019 and 2021. MRI examinations were independently evaluated for ten predefined imaging features. Diagnoses were established using histopathology or a validated clinical-radiological diagnostic algorithm. Interobserver agreement and diagnostic performance were analyzed using multivariable logistic regression, with internal validation performed using bootstrap resampling. Results: Interobserver agreement was excellent (kappa (kappa) = 0.81-1.0). Fluid-fluid level, hypointense rim on T2-weighted images, septation, and paramedian coronal location were significantly associated with cystic pituitary adenoma. In contrast, spontaneous T1-weighted hyperintensity, intracystic nodule, and midline sagittal location were more frequently observed in RCC. The final multivariable model demonstrated excellent discrimination (AUC = 0.91), with stable performance after bootstrap validation (optimism-corrected AUC = 0.88). Conclusions: The proposed MRI-based multivariable prediction model demonstrated high discrimination and provides a structured approach for estimating the probability of cystic pituitary adenoma using routinely available MRI features. Such an approach may help reduce unnecessary surgical interventions in patients with Rathke's cleft cyst while facilitating appropriate treatment planning for cystic pituitary adenomas. However, external validation in larger cohorts is required before routine clinical implementation.