Trust in AI is crucial for its adoption and acceptance, as it influences how users interact with and rely on AI systems. AI systems, which can learn and act autonomously, require trust to be developed and maintained. Trust in AI is influenced by various factors, including technical aspects such as accuracy, reliability, and transparency, as well as non-technical considerations like ethics, legal compliance, and social values. The study presents a systematic review of trust in AI, examining different types of human-machine interactions, the impact of trust on technology acceptance, and the development of metrics for trustworthy AI. It also discusses challenges in building trust, such as the black-box nature of AI and the need for explainability. The review highlights the importance of trust in AI for its successful implementation in various domains, including healthcare, finance, and autonomous systems. Trust in AI is not only a technical issue but also a social and ethical one, requiring a balance between technical performance and ethical considerations. The study proposes a framework for evaluating trustworthy AI, emphasizing the need for transparency, explainability, and accountability. It also identifies key factors that influence trust, such as the perceived reliability of AI systems and the social context in which they are used. The review concludes that trust in AI is essential for its development and application, and that future research should focus on improving trust through better transparency, explainability, and ethical guidelines.Trust in AI is crucial for its adoption and acceptance, as it influences how users interact with and rely on AI systems. AI systems, which can learn and act autonomously, require trust to be developed and maintained. Trust in AI is influenced by various factors, including technical aspects such as accuracy, reliability, and transparency, as well as non-technical considerations like ethics, legal compliance, and social values. The study presents a systematic review of trust in AI, examining different types of human-machine interactions, the impact of trust on technology acceptance, and the development of metrics for trustworthy AI. It also discusses challenges in building trust, such as the black-box nature of AI and the need for explainability. The review highlights the importance of trust in AI for its successful implementation in various domains, including healthcare, finance, and autonomous systems. Trust in AI is not only a technical issue but also a social and ethical one, requiring a balance between technical performance and ethical considerations. The study proposes a framework for evaluating trustworthy AI, emphasizing the need for transparency, explainability, and accountability. It also identifies key factors that influence trust, such as the perceived reliability of AI systems and the social context in which they are used. The review concludes that trust in AI is essential for its development and application, and that future research should focus on improving trust through better transparency, explainability, and ethical guidelines.