The deployment of Artificial Intelligence (AI) systems is going on across different critical infrastructures. No one is thinking of its trustworthiness. There is an urgent need for trustworthy AI. This chapter outlines many ideas behind trustworthy AI systems. This chapter discusses trustworthy AI in terms of accountability, transparency, explainability, fairness, privacy, robustness, and safety. We did a systematic review of integrating these principles in the AI development lifecycle. This chapter also surveys technical tools and evaluation metrics that are used to build trustworthy AI systems. This chapter offers a roadmap for ML engineers to develop responsible AI systems. Also, this chapter discusses the need of quality metrics and evaluation methods for determining the success of the trustworthy AI technical implementation. At the end of this chapter, we briefly discussed the future scope of trustworthy AI and opportunities for better implementations that could lead to better frameworks delivering with increased confidence.