This research focuses on innovatively predicting crab age using machine learning techniques, relying heavily on their physical attributes such as sex, length, diameter, height, and various weight measures. With commercial crab farming as a backdrop, the objectives set forth include the development of a robust predictive model that can harness a textual dataset in CSV format, implement advanced feature engineering techniques to enhance accuracy, and apply classical regression analytics to understand the relationships between these physical features and age. To achieve these goals, the research employs rigorous methodologies such as data preprocessing, extensive feature engineering, exploration of various regression algorithms, model training and evaluation, and feature importance analysis. This approach is underpinned by the significance of aiding crab farmers in their harvest decisions, contributing to sustainable aquaculture, and understanding the interrelation between crab size, weight, and age. Preliminary reviews of past literature indicate a gap in utilizing such an approach, making this study paramount in the realm of sustainable crab farming. The outcomes of this research not only serve the aquaculture industry but also pave the way for future studies, highlighting the vast potential of machine learning techniques in real-world scenarios.