Legal cases involve specific terminology, use past judgments as references, and the entire legal process is expensive, both in terms of time and money. Further, it is not clear at the outset whether the expected judgment will prevail. In the context of trademark and copyright cases, the present paper develops a rule-based system that can be useful, for both lawyers and litigants, as an assisting tool to predict outcomes. The paper proposes a forecasting framework involving TF-IDF weighting scheme, Fuzzy C-means algorithm for clustering, the construction of decision trees using Gini Impurity Measure, and using Takagi–Sugeno fuzzy controller for efficient prediction. The dependent variable is binary, and we observe that the combination of specific words and their relative importance has a bearing on the judicial outcome. The paper goes beyond predicting outcomes based on relevant features, and suggests specific rules leading to outcomes of legal proceedings. Accuracy, Balanced Accuracy, Precision, Recall, and F-beta are used as forecasting efficiency metrics and the results indicate moderate forecasting efficiency.