With technology scaling approaching atomic levels, IC test and diagnosis of complex System-on-Chips (SoCs) become overwhelming challenging. In addition, sustaining the reliability of transistors as well as circuits at such extreme feature sizes, for the entire projected lifetime, also become profoundly difficult. This holds even more when it comes to emerging technologies that go beyond convectional CMOS in which the underlying physics are not yet fully understood. In this special session paper, we describe the usage of machine learning in several test and reliability related areas. First, we demonstrate the vital role that machine learning can play in IC test showing the importance of explainability as a frontier for machine learning in IC test. Afterwards, we discuss how novel physics-informed neural networks can be employed to model electrostatic problems in VLSI designs. This is essential to mitigate the deleterious effects of of time dependent dielectric breakdown, which is the key source of reliability degradations. Finally, we discuss the major sources of reliability degradations at the transistor level in advanced technology nodes such as transistor aging phenomena and self-heating effects as well as we demonstrate how machine learning approaches can further help in developing reliable emerging technologies.