rtificial intelligence algorithms are among the most important modern technologies that contribute to the development of various fields such as medicine, education, and industry. However, implementing these algorithms involves several technical challenges that affect their efficiency and effectiveness. One of the main challenges is data quality and availability, as AI algorithms rely heavily on data for learning and decision-making. Inaccurate or insufficient data can lead to incorrect or biased results. Moreover, data collection and preprocessing require significant time and effort nother challenge is the complexity of algorithms and the need for high computational resources. Many AI models require powerful processing capabilities and large memory, which increases costs and limits their use in certain environments. In addition, AI algorithms face challenges related to interpretability and transparency, as some models operate as “black boxes,” making it difficult to understand how decisions are made. Issues such as integration with legacy systems and cybersecurity concerns also represent major technical challenges