In many languages abbreviations are very common and are widely used in both written and spoken language. However, they are not always explicitly defined and in many cases they are ambiguous. This research presents a process that attempts to solve the problem of abbreviation ambiguity using modern machine learning (ML) techniques. Various baseline features are explored, including context-related methods and statistical methods. The application domain is Jewish Law documents written in Hebrew and Aramaic, which are known to be rich in ambiguous abbreviations. Two research approaches were implemented and tested: general and individual. Our system applied four common ML methods to find a successful integration of the various baseline features. The best result was achieved by the SVM ML method in the individual research, with 98.07% accuracy.