The integration of Artificial Intelligence (AI) into the field of optometry marks a transformative era in eye care, offering significant advancements in diagnosis, treatment planning, and patient management. AI technologies, particularly machine learning (ML) and deep learning (DL), have demonstrated remarkable capabilities in analyzing large volumes of ocular data with precision comparable to, and sometimes surpassing, human experts. Early studies, such as those by Gulshan et al. (2016) and Ting et al. (2017), laid the groundwork by validating the use of deep neural networks for the detection of diabetic retinopathy from retinal fundus photographs, achieving sensitivity and specificity rates that matched clinical standards. These pioneering efforts revealed the potential for AI not only in disease screening but also in enabling mass community outreach programs, especially in resource-limited settings.In optometric practice, AI has expanded its role beyond retinal imaging. Applications now extend to the diagnosis of refractive errors, glaucoma screening, keratoconus detection via corneal topography, and dry eye disease assessment through meibography analysis. Algorithms trained on anterior segment imaging have shown promise in the early detection of conditions that traditionally rely on subjective evaluation by clinicians. Studies before 2021, including those from the Moorfields Eye Hospital collaboration with DeepMind Health, demonstrated how AI could assist in triaging urgent ocular conditions based on optical coherence tomography (OCT) scans, thereby reducing waiting times and optimizing specialist referrals. Additionally, AI is shaping the future of personalized eye care. Predictive analytics based on patient demographics, genetic information, lifestyle factors, and longitudinal clinical data allow for tailored management strategies, thereby improving patient outcomes. In myopia management, for instance, AI models can predict the risk of high myopia progression in children, guiding early intervention decisions. Despite its vast potential, the integration of AI into optometry faces certain challenges. Concerns regarding data privacy, ethical implications, algorithmic biases, and the lack of standardized regulatory frameworks remain areas that require careful attention. Furthermore, studies highlight that while AI can augment clinical decision-making, it should not replace the critical role of optometrists. Human oversight is essential to interpret AI outputs contextually, considering the patient's holistic health profile and socio-emotional needs. The COVID-19 pandemic further accelerated the adoption of tele-optometry and AI-driven remote screening tools, emphasizing the need for flexible and resilient eye care delivery systems. Retrospective analyses have shown that AI-assisted teleophthalmology programs maintained screening continuity during periods when in-person consultations were restricted, underlining the system’s value beyond traditional clinical settings.