Polymers are versatile and significant components of the modern material industry. They offer unique properties and are indispensable in varied sectors, including manufacturing, healthcare and infrastructure. Plastics, derivative of these polymers, are omnipresent and contribute expansively to everyday life and industrial sectors. They are highly ubiquitous due to their cost-effective, malleable, and durable design. The polymer industry has heavily relied on Edisonian methods to innovate products like plastic. These approaches were labour-intensive, expensive and required prolonged research. While these methods have led to revolutionary innovations, they are resource-intensive and limited in their ability to generate complex polymeric designs. The integration of Artificial Intelligence into polymeric product designing has marked a new era in the polymer industry. The AI-generated data-driven methods help to bridge the gap between theoretical models of polymers and their empirical observations. They assist in material design and discovery, property prediction and optimisation, manufacturing processes, and categorisation, while also uncovering insights for sustainable polymer generation. These methodologies encompass various Machine Learning and Deep Learning. A brief insight into the basics of artificial intelligence, data-driven models and polymeric designing has been introduced and discussed in this chapter. These methods not only modernize design processing but also enable simulation and real-time monitoring of the polymer products. By overcoming the shortcomings of empirical methods through advanced computational techniques, artificial intelligence is aiming to overcome longstanding challenges associated with polymer formulation for creating sustainable polymer products.