Decision-making in management has evolved from intuition-dominated judgment toward more structured, evidence-based, and analytics-supported processes. Yet many organizations still struggle to integrate strategic judgment, quantitative evaluation, uncertainty treatment, and governance considerations into a single coherent decision architecture. This paper develops an integrated framework for managerial decision-making that combines bounded rationality, evidence-based management, Multi-Criteria Decision-Making (MCDM), decision support systems, and human–AI collaboration. The study first synthesizes the theoretical foundations of managerial decision-making and then proposes a professional five-stage framework for screening, weighting, scoring, validating, and governing strategic alternatives. To demonstrate the logic of the framework, an illustrative management case is presented for project portfolio selection under financial, operational, strategic, and risk criteria. The model employs normalized criterion scores, weighted utility aggregation, evidence credibility assessment, and governance alignment adjustments. The numerical illustration shows that alternatives with the highest standalone financial attractiveness are not always optimal once implementation time, strategic fit, evidence quality, and risk exposure are jointly considered. The paper contributes to management literature in three ways: 1) it bridges classical and contemporary decision theories into a unified managerial structure, 2) it offers a transparent mathematical model that managers can adapt to real organizational settings, and 3) it demonstrates how human judgment and algorithmic support can be combined without fully delegating strategic control to automated systems. The findings suggest that effective managerial decision-making depends not only on analytical sophistication but also on process quality, evidence integrity, and organizational alignment. These insights are particularly relevant for senior managers facing high-stakes choices in dynamic and uncertain environments. Real references below were verified from publisher or scholarly records.