Water quality is one of the most important aspects in giving rise to healthy and productive aquaculture. This work examines water quality based on a rule-based classification scheme on three parameters: pH within the ideal range from 6.5 to 8.5, DO within the range of 4–12 mg/L, and temperature in the range of 20 to 30 °C. Water quality will be classified by this framework into five different levels, namely Very Low, Low, Medium, High, and Very High, based on established standards from industry. These are then combined into an overall quality score through a weighted scoring system that clearly and actionably describes the data insights. The preprocessing of the data was done for enhancing accuracy, treating the missing values, and normalizing the data. Visualization to show the distribution of categories of water quality was performed using Python’s libraries such as Matplotlib and Seaborn. Then, it is simplified by creating color-coded output in Excel to show results fast and in a more user-friendly way. This approach would surely ensure that aquaculture management has an operable and effective way of conducting its activities in informed decision-making while shunning inaccuracies.