Background/Objectives: Diagnosing interstitial pregnancy (IP) using ultrasonography can be challenging, as it is often mistaken for eccentrically located intrauterine pregnancy (IUP). In this retrospective cohort study, we aimed to develop a predictive scoring model using multiple clinical factors to enhance the diagnosis of IP and facilitate timely interventions in suspected cases. Methods: We enrolled 63 pregnant women with a diagnosis of suspected IP who visited a single tertiary center between January 2006 and December 2023. Data on the clinical risk factors, symptoms, laboratory test results, and ultrasound findings were analyzed. A statistical predictive score was developed using logistic regression analysis with feature selection based on the least absolute shrinkage and selection operator to optimize the predictive accuracy and clinical applicability. Results: From a total of 12 factors, a scoring model was constructed from the three most prominent factors—ultrasound findings showing no surrounding endometrium, myometrial thinning of less than 5 mm, and vaginal bleeding—all of which demonstrated high feature importance. This predictive score identified IP with a negative predictive value of 0.950 in the low-risk group and a positive predictive value of 1.000 in the high-risk group, whereas the overall area under the curve was 0.998 (95% confidence interval, 0.992–1.000). Conclusions: The statistically derived predictive model––ultrasound showing no surrounding endometrium and myometrial thinning < 5 mm combined with vaginal bleeding––demonstrated high accuracy and practical applicability for IP diagnosis, providing a robust tool to enhance clinical decision-making and optimize routine management strategies for IP.