The Internet of Things (IoT), a network of interconnected devices enabling seamless data sharing, has seen widespread adoption driven by the increasing reliance on smart technologies. However, this rapid growth introduces significant security vulnerabilities, particularly concerning sensitive IoT data and the integrity of connected devices. While artificial intelligence (AI)-based intrusion detection systems (IDS) have proven effective in identifying malicious activities, their complex architectures often hinder security administrators from fully understanding and utilizing them. To bridge this gap, explainable artificial intelligence (XAI) has emerged as a critical tool for interpreting intricate model structures and identifying key features. This paper presents: (1) an analysis of major IoT vulnerabilities and common attack types across different application domains, (2) a review of XAI algorithms used in IoT intrusion detection systems and their benefits, and (3) a discussion of the limitations of widely used XAI techniques along with future research directions.