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The rapid integration of Artificial Intelligence (AI) across industries is fundamentally reshaping occupational structures and redefining employment dynamics. This study presents an evidence-based analysis of AI-driven job transformation and associated employment risks through a systematic review of recent literature from major academic databases. The paper synthesizes sector-specific insights to examine how AI influences task automation, job augmentation, and skill requirements across domains such as manufacturing, information technology, healthcare, and finance. A structured methodology is adopted to identify research gaps, particularly the lack of comparative sectoral assessments and standardized risk evaluation frameworks. The findings reveal that routine-intensive sectors exhibit higher susceptibility to automation, while knowledge-driven domains experience significant augmentation and skill shifts rather than displacement. Furthermore, the study proposes a sectoral risk classification to better understand vulnerability patterns and workforce implications. The results highlight the growing importance of re-skilling and adaptive policy measures to mitigate employment risks. This work contributes by integrating fragmented literature into a coherent, comparative perspective, offering actionable insights for researchers, policy makers, and industry stakeholders in navigating the evolving future of work.