The chapter critically tackles a prevalent issue in health research by looking at how illnesses develop in males, which puts women in danger of health problems. AIML-based methods are revealing several differences in sex-based health research. To date, few studies used AI to investigate sex-related differences using unstructured text, despite the technology’s extensive use in health research. The chapter highlights the gradual correction process and recommended practices for using gender-based data analytics to reduce prejudices and examines the revolutionary effects of large data sets on the identification of diseases, patient care, and the use of resources in the healthcare sector. Predictive statistical analysis provides several advantages in healthcare, including real-time, precise insights, data-driven, effective therapy, accurate diagnoses, and tailored medicines. The need for precision is examined, including how to integrate environmental and genetic elements with a variety of AI technologies to provide customized therapies. Nevertheless, bias detection is frequently overlooked by widely used biomedical AI systems, producing less-than-ideal results. The chapter examines gender and sex disparities and offers suggestions for maximizing technology use to improve global health outcomes and reduce inequality. The healthcare industry may get important gender-based insights for a more complex and fair response to health issues by using predictive analytics.