The field of genetic analysis is witnessing rapid advancement due to the advanced integration of biotechnology and artificial intelligence, making the analysis of vast genomic datasets more efficient and precise than ever before. Traditional genetic analysis relies on statistical tools and complex laboratory procedures that require significant time to process and compare genetic sequences. However, the introduction of machine learning and deep learning algorithms has created a qualitative shift in the ability to decode genetic information and understand its complex biological patterns. Artificial intelligence algorithms accelerate genetic analysis through their capacity to process large-scale sequencing data generated by next-generation sequencing technologies, which can produce millions to billions of genetic reads in a single experiment. Advanced computational models sort these reads, align them with reference genomes, and detect mutations and structural variations with high efficiency and in significantly reduced time compared to conventional methods. AI systems can also identify recurring patterns and nonlinear relationships between genetic variants and associated traits or diseases, thereby shortening lengthy manual analysis stages and minimizing the likelihood of human error. Furthermore, deep learning techniques contribute to interpreting non-coding regions of the genome that were previously less understood in terms of function. Artificial neural networks can analyze complex interactions between genes and regulatory elements and predict the impact of mutations on gene expression or protein structure. This predictive capability enhances the understanding of molecular mechanisms underlying genetic disorders and supports the development of targeted therapeutic strategies based on individual genetic characteristics. In terms of improving the accuracy of DNA tests, artificial intelligence plays a critical role in reducing false-positive and false-negative results by refining mutation detection algorithms and genetic variant classification. Machine learning–based classification models are used to determine whether a genetic variant is pathogenic or benign, relying on extensive databases and multiple clinical precedents. These models can also integrate genetic data with clinical and environmental information to enhance the reliability of final interpretations. Artificial intelligence also strengthens quality control in laboratory testing by monitoring sequencing processes, verifying read accuracy, and detecting potential technical deviations. Such digital oversight reduces variability between laboratories and standardizes analytical procedures, thereby increasing confidence in genetic testing outcomes across medical diagnostics, forensic applications, and population studies. Integrating artificial intelligence into genetic analysis extends beyond accelerating technical processes; it redefines the methodology of genomic research itself by shifting from descriptive observation-based approaches to predictive modeling powered by intelligent systems. As a result, more accurate diagnoses can be achieved, personalized treatment plans can be developed, and the principles of precision medicine can be further advanced, reinforcing the role of modern technology in enhancing healthcare quality and deepening our understanding of the human genetic structure. Al-Mustaqbal University is the number one university in Iraq.