Artificial intelligence (AI) is transforming the field of genetics by enabling more accurate diagnosis, personalized treatment, and efficient research. AI, including machine learning (ML) and deep learning (DL), is being applied to various aspects of genetics, such as genome sequencing, gene therapy, and drug discovery. AI can analyze large-scale genetic data to identify hereditary disorders, predict genome editing outcomes, and improve drug development. Deep learning, a subset of AI, is particularly useful for autonomous feature extraction and has shown promise in both basic and applied genetic research. AI is also being used to enhance the accuracy of echocardiography and other diagnostic tools, and to develop predictive models for genetic diseases. However, the integration of AI in genetics faces challenges, including the need for large, high-quality datasets and the ethical implications of data privacy and security. Despite these challenges, AI has the potential to significantly improve precision medicine and genetic disease treatment. The review highlights the current applications of AI in genetics, discusses the potential future developments, and emphasizes the importance of collaboration between geneticists, physicians, and AI experts to fully realize the benefits of AI in the field of genetics.Artificial intelligence (AI) is transforming the field of genetics by enabling more accurate diagnosis, personalized treatment, and efficient research. AI, including machine learning (ML) and deep learning (DL), is being applied to various aspects of genetics, such as genome sequencing, gene therapy, and drug discovery. AI can analyze large-scale genetic data to identify hereditary disorders, predict genome editing outcomes, and improve drug development. Deep learning, a subset of AI, is particularly useful for autonomous feature extraction and has shown promise in both basic and applied genetic research. AI is also being used to enhance the accuracy of echocardiography and other diagnostic tools, and to develop predictive models for genetic diseases. However, the integration of AI in genetics faces challenges, including the need for large, high-quality datasets and the ethical implications of data privacy and security. Despite these challenges, AI has the potential to significantly improve precision medicine and genetic disease treatment. The review highlights the current applications of AI in genetics, discusses the potential future developments, and emphasizes the importance of collaboration between geneticists, physicians, and AI experts to fully realize the benefits of AI in the field of genetics.