Seismic interpretation has been a cornerstone of geoscientists workflows. Considering complexgeological structures, conventional fault interpretation technique requires digitizing fault on multipleseismic sections based on the continuity of the reflectors and amplitude strength. This process ishighly subjective, time consuming and frequently produce an inconsistent interpretation.Machine learning (ML) assisted fault interpretation approach is a geoscience driven workflow whichprovides a consistent prediction of fault locations and geometries enabling geoscientist to buildconsistent structural framework model with reduced uncertainties in an efficient manner and savingimmense amount of time. I will share perspectives on how machine learning (ML) approach improves fault interpretation usingcase studies examples.