Introduction: There is a growing trend toward induction of labor and approximately 20% of labors are induced. The induction of labor is not always successful and carries risks. Models for predicting the outcome of induction of labor are necessary, based on the individual characteristics of patients and which can be used with high reliability when deciding on the method of delivery. Isolating pregnant women who are at high risk of failed induction of labor would lead to a reduction in maternal and neonatal morbidity and mortality. Objective: The main aim of this study was to develop a machine-learning-based model for predicting the success of labor induction. To that end, the clinical and ultrasound parameters that affect the successfulness of labor induction were assessed, and a new ultrasound scoring system (USS) was developed and then assessed. Study design: This prospective randomised study from a single centеr included 226 term women who underwent induction of labor. First, a wide range of clinical and ultrasound pre-induction parameters were recorded. The induction was initiated by endocervical administration of Prepidil® gel (for Burnett score <6) or with intravenous Oxytocin (for Burnett score ≥ 6). After evaluation of ultrasound parameters, we created ultrasound scoring system. Finally, a comprehensive model using machine learning algorithms for predicting the success of the induction of labor was developed. A Stratified KFold cross-validation technique was used to check the developed model internally and to prevent overfitting. Results: The majority of pregnant women were delivered vaginally – 158 (69.9%), while 68 (30.1%) pregnant women were delivered by caesarean section. The most common indication for labor induction was postterm pregnancy – 124 (54.87%), and the most common method of labor induction was a combination of amniotomy and oxytocin – 159 (70.35%). In terms of clinical parameters, this study found that induction of labor correlates with parity, BMI (both at p<0.05) and the Burnett score (p<0.001). All ultrasound parameters were statistically significant (p<0.05) except for the posterior cervical angle (p=0.861) and ultrasound-estimated fetal weight (p=0.766). The parameters included in the USS were comparable to the Burnett score parameters. Therefore, the new USS encompasses the following five ultrasound parameters: cervical length, the size of the posterior cervical angle, the funneling (if present), the position of the fetal occiput and the distance from the head of the fetus to the outer cervical os. In all cut off values, USS showed higher sensitivity, and Burnet score showed higher specificity. In relation to predicting the time from the start of labor induction to delivery itself, USS showed better results compared to the Burnett score (0.482 vs 0.069). The value of the area under the ROC curve, was higher for USS (AUC 0.734) compared to Burnett score (AUC 0.66). The best results were obtained with the combination of Burnett score+USS+clinical parameters (AUC 0.920). A decision tree and application software for predicting the success of labor induction were created, in order to enable the simplest possible use in daily work as well as external validation of the model. Conclusion: The ultrasound scoring system is simple, easy to use, has high sensitivity and specificity, which makes it easy to apply in daily clinical work. The findings imply that the model developed in this study, which takes into account clinical parameters, the ultrasound parameters and the Burnett score and uses machine learning algorithms, yields better results than models using other parameters. Nevertheless, further research is needed to overcome the limitations of the present study and confirm the reliability of the model.