Resident attrition is a growing concern in medical education systems worldwide. This article analyzes key personal, institutional, economic, and vocational factors associated with early departure from residency training programs. A structured narrative review of studies published between 2000 and 2025 was performed. A structured narrative review was conducted using PubMed, Scopus, SciELO, and Google Scholar. Additional analytical tools were used, including heatmaps, interaction matrices, influence diagrams, and a multivariate AI‑based predictive model. Five residents who had entered one of thirteen medical specialties at a general hospital belonging to the Mexican Ministry of Health, in the city of León, located in central Mexico, were interviewed within the first 15 days after voluntarily requesting . open-ended interview. The interviews were conducted by the medical coordination and for teaching and research. Three were from Internal Medicine, one from Geriatrics, and one from General Surgery. The average age was 26 ± 1 years. In Internal Medicine, three men indicated that they were married and could not afford a study abroad program; the other was single, which was also the reason for his withdrawal literature review Findings demonstrate that burnout, excessive workload, vocational mismatch, institutional mistreatment, and socioeconomic pressures significantly predict attrition risk. A predictive model based on artificial intelligence is proposed to identify high‑risk residents. Strengthening well‑being programs, improving structural working conditions, and integrating vocational assessment into specialty selection may reduce early attrition.