This study investigated the perception of machine and deep learning among academic teachingstaff at Assiut University's Faculty of Nursing. Defining machine learning as a subset of artificial intelligence and deep learning as multi-layered neural networks, the study employeda descriptive cross-sectional design. A convenience sample of teaching and assistant teachingstaff completed questionnaires about their personal and job characteristics, as well as their understanding of machine and deep learning. The results showed a significant difference inperception: The vast majority of assistant teaching staff reported satisfactory understanding, compared to more than half of teaching staff reporting unsatisfactory understanding(P=0.001). The study concluded that assistant teaching staff demonstrated a significantlyhigher level of satisfaction and understanding of machine and deep learning comparedtoteaching staff, with statistically significant differences across all measured dimensions. Theresearchers recommended implementing educational programs for professors and assistant professors to improve their knowledge of these technologies, and for educational institutions to use this information to inform hiring, training, and evaluation practices, as well as theapplication of these technologies in the teaching process.