In today’s rapidly evolving labor market, understanding how professionals can build sustainable career trajectories is increasingly important. There are various studies researching career breaks in academic and related fields, but there is a lack of comprehensive studies on the impact of career breaks in the professional field. This study addresses the critical gap in understanding post-break career trajectories through a large-scale analysis of 9,044 LinkedIn profiles containing 7,256 career breaks. We develop a multidimensional analytical framework examining temporal patterns (break duration, career stage), industrial transitions (20 sectors), and positional changes to quantify break outcomes. Our dataset, refined through rigorous filtering for country representation, break duration (<60 months), and work history completeness, reveals that 65.1% of professionals change industries after their first break, with Technology sector workers experiencing both high promotion (22.4%) and demotion (31.0%) rates. Using 15 engineered features, including pre-break tenure (μ = 12.4 months), career stage (μ = 5.2 years), and industry switches, we compare five machine learning classifiers to predict post-break outcomes. The Random Forest and XGBoost models achieve superior performance (66.1% accuracy). Statistical analyses uncover duration-outcome relationships, showing industry changers take significantly longer breaks (11.2 vs 9.8 months, p = 0.032) and that second breaks yield peak promotion rates (26.6%, p = 0.003). Our findings challenge conventional assumptions about career interruptions, demonstrating that strategic timing and sector context can mitigate traditional penalties. This research provides professionals, employers, and policymakers with data-driven insights for navigating career breaks, supported by 13 analytical visualizations including industry transition heatmaps and duration-outcome distributions.