The domain of materials science and engineering, especially polymer science is beholding a notable transition in the rate of discovery, predominantly influenced by the integration of data-driven informatics approaches, which denotes a growth towards more anticipatory, efficient, and interdisciplinary research. Polymers are highly complex and versatile structures. Due to the wide-ranging attributes and versatility, polymeric materials are extensively used worldwide in numerous areas like medicine, consumer goods, aerospace, packaging of plastics, pot and pan handles, soft drinks bottles, and electronics. Classical approaches for synthesizing polymers are usually time-consuming and resource-demanding; Nevertheless, data-driven approaches facilitate the speedy exploration of large-scale chemical and structural domains. The advent of data-driven approaches has led to the rise of a new domain called “Polymer Informatics”, which utilizes Artificial Intelligence (AI) and Machine Learning (ML) for the discovery and development of new polymers. In recent years, machine learning has garnered significant attention and has become a powerful tool in many research fields. Machine learning contributes to resolving the time-consuming trial-and-error technique in conventional methods. Despite notable development, polymer informatics faces challenges that restrain its growth towards durable material discovery. Therefore, the data-driven practices are more effective and workable than the time-consuming experimental procedures. Some successful attempts in polymer informatics have been made, such as for quick polymer prediction, surrogate models are trained on pre-existing polymer data, facilitating the screening of promising polymer candidates with definite target property requirements. Several tools and software programs have been developed and designed to foster the incorporation of machine learning in polymer science, which are available to assist data management, analysis, and modeling. Open source tools and platforms are also designed to benefit the researchers in synthesizing a novel polymer without any hindrance.