Internet addition is a type of mental disorder. It is the result of the excessive internet usage and is concern for the physical and psychological well-being. This paper employs machine learning techniques to understand, evaluate and predict the severity of internet addiction and its impact on health. For this purpose, a real dataset of “Internet Addiction and Mental Health among College Students in Malawi” has been considered. It consists of self-assessed response of 984 university student participants. That includes demographic, behavioral and health-related information. Based on this dataset, two type of relationship have been discussed (1) relation between “demographic features” and “health complexities” and (2) relation between “Internet usage behaviors” and “health complexity”. Next, the key features were identified through comprehensive data analysis. Additionally, there machine learning algorithms namely Backpropagation Neural Network, Random Forest, and C4.5 Decision Tree— were tested to identify ‘internet addiction’ in a subject with the four severity levels (0 to 3). According to results, the Random Forest classifier achieved the highest accuracy of 91%. Additionally, C4.5 algorithm has been used for extracting rules for predicting “Internet addiction” severity level. These rules are demonstrating a relation between “Internet usages pattern” and “Internet addiction severity level”. Additionally, these rules are easy to interpret and can be utilized as a practical tool for self-assessment towards Internet addiction and additionally beneficial for healthcare professionals.