Emerging technology markets, encompassing artificial intelligence, quantum computing, synthetic biology, and blockchain, are dynamic arenas of innovation characterized by rapid advancements and inherent uncertainties. These markets present transformative opportunities but also significant risks, including regulatory ambiguity, technological obsolescence, market volatility, and ethical dilemmas. Strategic decision-making in such contexts requires a robust framework to balance opportunity with risk, ensuring sustainable growth and competitive advantage. This paper proposes a conceptual framework for risk-informed strategic decisions, designed to address the multifaceted challenges of emerging technology markets. The framework integrates three core components: proactive risk identification, scenario-based analysis, and adaptive strategy formulation. It draws on interdisciplinary theories from decision science, risk management, systems thinking, and technology adoption to create a scalable, resilient model. Employing a mixed-method approach, the study synthesizes a systematic literature review, qualitative interviews with industry experts, and case study simulations to validate the framework’s applicability in AI and biotechnology markets. Key findings highlight the framework’s ability to enhance decision resilience by providing structured tools to evaluate trade-offs, mitigate uncertainties, and align stakeholder interests across diverse scenarios. Unlike traditional models, which often rely on static assumptions, this framework emphasizes dynamic adaptation, enabling organizations to navigate complex regulatory landscapes, technological disruptions, and competitive pressures. For industry leaders, it offers actionable insights for optimizing resource allocation, fostering innovation, and building strategic agility. Policymakers can leverage its principles to design regulations that balance risk mitigation with market growth, promoting responsible innovation. The study contributes to strategic management by addressing critical gaps in existing models, offering a practical, theoretically grounded tool for decision-makers in high-stakes environments. By fostering resilience and adaptability, the framework supports organizations in achieving long-term success in the unpredictable landscape of emerging technologies, with far-reaching implications for theoretical advancement and practical implementation across global markets.