The way biological systems are built and designed has been revolutionized by synthetic biology. Further enhancements like predictive modeling, optimization and systematic design of complex biological systems, is now possible due to integration of Artificial Intelligence into synthetic biology. This review shares insights on the role of AI in advancement of synthetic biology, including genome editing, metabolic pathway optimization and biological circuit design etc. AI-driven tools contribute to the increased efficiency and precision. Application of deep learning and machine learning has made it possible to make CRISPR-cas9, de novo protein design and gene circuit development more precise. However, there are still some persistent challenges, especially in curating high-quality biological datasets and bridging interdisciplinary gaps between computational and experimental scientists. Future perspectives focus on causal reasoning in AI models, integration of physics based algorithms, and promoting collaboration across disciplines to achieve breakthroughs in both synthetic biology and AI. By joining these fields, the transformative power of synthetic biology and AI can be unlocked and applied in the fields of medicine, biotechnology and environmental sustainability, pioneering a way for a new era of bioengineering.