<p>Machine vision systems have been increasingly deployed across domains such as healthcare, surveillance, autonomous vehicles, and industrial automation. While prior research has extensively focused on quantitative measures of system performance, there has been limited understanding of how end-users and stakeholders perceive, trust, and adopt these technologies. This study employed a qualitative approach to explore human-centered perspectives on machine vision, emphasizing issues of usability, trust, and ethical implications. Through semistructured interviews conducted with professionals from healthcare, security, and technology sectors, the study revealed lived experiences and perceptions of machine vision tools. The findings highlighted critical insights into user acceptance, challenges related to transparency, workflow integration, and fairness, and suggested pathways for more ethical and trustworthy deployment of machine vision technologies. By centering human experiences, this study complements performance-driven research and contributes to bridging the gap between technical development and real-world adoption.</p>