The main goal of this thesis work is to identify the root cause of different types of casting defects in the production of cylinder heads for a Swedish manufacturing company. This research helps the company choose suitable methods to produce with higher performance, remove casting defects, and improve their efficiency. This work will involve developing and implementing interpretable Machine Learning (ML) and Rule-Based approaches to Root Cause Analysis (RCA). Statistical analysis of the casting data is required to understand and identify the distribution of casting defects and process parameters. This analysis will enable the detection of outliers and errors. Standard data imputation methods will fill in the missing parameter values. Several oversampling methods, such as SMOTE and ADASYN, are implemented in this research. Comparing the effectiveness of these methods on casting data is essential to fix an imbalanced dataset. Machine learning models such as Random Forest (RF) will be trained to classify the processed data. Finally, interpretable rules that explain the root causes for various defect types must be extracted from the rule extraction techniques.