This systematic review examines the role of Artificial Intelligence (AI), Machine Learning (ML), and their combined applications in promoting organizational innovations across business and management contexts. Thirty papers published between 2020 and 2025 were systematically reviewed, comprising ten papers each focused on AI, ML, and AI-ML integration respectively. The analysis reveals that these technologies, alongside emerging innovations like blockchain and Internet of Things (IoT), significantly influence organizational innovation through multiple mechanisms and contexts. AI demonstrates particular strength in innovation analytics, business model innovation, and circular economy applications, while ML excels in knowledge management, customer retention strategies, and cybersecurity innovation. Their convergence, often with complementary technologies, enables transformative innovations across diverse sectors including healthcare, manufacturing, supply chain management, and financial services. Several novel frameworks identified in the review provide valuable insights into innovation development processes and organizational value creation. However, widespread adoption faces substantial barriers including data privacy concerns, transparency and reliability issues, significant skill deficits, limited organizational capabilities, and challenging business environments. The review identifies critical gaps in empirical research and highlights the predominance of poor-quality studies lacking methodological rigor. Future research directions emphasize the need for more empirical investigations, mixed-method approaches, cross-country comparative analyses, and theoretical development to advance understanding of technology-enabled innovation