Locks play a crucial role in Java-based multi-threaded applications, offering an effective solution for synchronizing shared resources. Yet, mishandling locks and threads can result in contention, leading to performance deterioration and compromising the scalability of Java applications. After conducting a thorough background study from various sources, we have identified and defined eight Java lock contention antipatterns. These antipatterns comprehensively represent the diverse scenarios of lock contention within intrinsic locks. Currently, performance engineers use legacy tools and their experience to identify and locate the source of these lock contention faults. In this study, several machine learning algorithms were evaluated based on their capability to identify the lock contention anti-pattern as the source of the lock contention. When the algorithms were trained and tested with a 70-30 split we got an accuracy above 90%. To validate the findings we used the Dacapo benchmark as the testing set and found XGBoost performing well among the rest with an overall accuracy of 87%. Logistic Regression, Random Forest and Support Vector Machine were the best performing models in terms of precision and recall values. We also validated our recommendations provided by comparing three important performance metrics between the original and refactored version of the antipatterns.