The advancement of artificial intelligence (AI) has profoundly altered the diagnostics of automotive systems, providing sophisticated approaches to improve the precision, speed, and efficiency of fault detection and resolution processes. However, the diversity of available tools and technologies presents challenges in terms of optimal selection, integration and scalability for industrial applications. This systematic literature review (SLR) aims to identify, analyze, and evaluate recent advancements and AI-based approaches utilized in diagnosing automotive systems. The methodology employed involves a rigorous examination of scientific and technical publications, with relevant articles selected from reputable databases. The study emphasizes the most widely used tools and technologies, including machine learning, deep neural networks, on-board diagnostics (OBD), as well as the Internet of Things (IoT), and predictive maintenance. Furthermore, a bibliometric analysis was conducted using NVivo to explore emerging research trends and relationships between key concepts. Lastly, the study discusses current challenges, limitations, and future directions for AI-driven automotive diagnostics, underscoring the importance of real-world validation and industrial feasibility.