Catheterization is a procedure used to diagnose and treat various cardiovascular diseases. Intracardiac echocardiography (ICE) is an emerging imaging modality that has gained popularity in these procedures due to its ability to provide high-resolution images of the heart and its surrounding structures in a minimally invasive manner. However, given its limited field of view, its orientation within the heart is difficult to judge simply from observing the acquired images. Therefore, ICE catheter tracking, which requires six degrees of freedom, would be useful to better guide interventionalists during a procedure. This work demonstrates a machine learning-based approach that has been trained to predict the roll angle of an ICE catheter using landmark scalar values extracted from bi-plane fluoroscopy images. The model consists of two fully connected deep neural networks that were trained on a dataset of bi-plane fluoroscopy images acquired from a 3D printed heart phantom. The results showed high accuracy in roll angle prediction, suggesting the ability to achieve 6 degrees of freedom tracking using bi-plane fluoroscopy that can be integrated into future navigation systems embedded into the c-arm, integrated within an AR/MR headset, or in other commercial navigation systems.