Steroid use, particularly anabolic–androgenic steroids (AAS), is associated with large range of potential side effects that can adversely impact an individual's health. These synthetic substances, designed to imitate the effects of testosterone, can disrupt the delicate hormonal balance within the body. Common side effects include dermatological issues such as acne and hair loss, along with disruptions in mood and fertility. Prolonged or excessive use of steroids poses serious risks to vital organs, including the liver, lungs, kidneys, and heart, increasing the potential risk conditions such as liver disease, stroke, and cardiovascular complications. Understanding and addressing these potential side effects is crucial for informed decision-making in medical contexts involving steroid usage. Machine learning algorithms play a pivotal role in identifying and understanding the side effects of steroids, contributing significantly to the field of healthcare and patient well-being. The complexity of steroid interactions within the human body, coupled with the diverse range of potential side effects, makes it challenging to predict and manage these outcomes through traditional methods alone. Machine learning algorithms excel in handling large datasets, allowing them to analyze intricate patterns and relationships within patient data, medical records, and relevant parameters. By employing these algorithms, researchers and healthcare professionals can identify correlations between steroid usage and specific side effects, providing insights into risk factors, dosage thresholds, and individual susceptibility. This predictive capability not only enhances our understanding of the potential health hazards associated with steroids but also enables early detection and intervention. The ML-based methodology for identifying health hazards due to steroid use involves collecting a large dataset, selecting relevant features, pre-processing the data, training a machine learning model, evaluating the model’s performance, deploying the model in a real-world setting, and monitoring and refining the model over time.