This research paper addresses the critical issue of understanding the diverse applications of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) across 14 distinct engineering disciplines, a topic of growing significance as these technologies increasingly drive innovation in engineering. The study is motivated by the need to clarify the hierarchical relationships and specific roles of AI, ML, and DL within these fields, which remains a challenge in the current literature. A systematic review methodology was employed, analyzing 150 peer-reviewed studies to identify the prevalence and effectiveness of AI, ML, and DL techniques. The analysis considered key factors such as the specific technique used (AI, ML, or DL), the tools or software employed, the methodologies adopted, and the outcomes reported. Notably, ML emerged as the predominant technique, utilized in approximately 73% of the studies. This preference for ML is attributed to its versatility across various domains, refined algorithms developed over decades, and superior interpretability compared to DL. The findings suggest that ML's widespread adoption in engineering research is due to its ability to provide actionable insights across multiple disciplines, with DL being applied primarily in cases where high-dimensional data or complex pattern recognition is essential. The research highlights the significant contribution of ML in enhancing predictive accuracy and optimizing engineering processes, underscoring its dominance in contemporary studies. This study goes beyond previous efforts by not only providing a comprehensive overview of AI, ML, and DL applications but also elucidating the hierarchical relationships between these technologies. It clarifies that AI encompasses the broader field, ML focuses on algorithms that enable systems to learn from data, and DL represents a subset of ML using neural networks for complex feature extraction. The novelty of this work lies in its systematic comparison of these techniques across a wide range of engineering disciplines, offering new insights into their respective contributions and interactions. These findings provide a clearer framework for understanding how AI, ML, and DL can be effectively leveraged in engineering research, guiding future studies and applications in the field.