Saccharum spp. is one of the most important crops in the tropics and an important plant in the agro-economy of several countries. Its production involves commercially making sugar from the juice extracted from the plant’s stalks or producing biofuels from the resulting molasses. Economic sugarcane breeding is time-consuming owing to its polyploid nature, sterility of superior clones, long growth period, and complex interactions of varietal diversity with environmental factors. Improvement strategies in crops are changing to enhance sugar yield per hectare. The particular relevance of sugarcane breeding is to select genotypes that improve sucrose yield and sugar quality characteristics, enabling the plants to be more resistant to pests and diseases. Over the past decade, tropical sugarcane genotypes have been bred to improve sugarcane yield and economic viability. Generally, different parts of the world have developed sugarcane breeding programs to increase sugar productivity as well as guarantee high sugar quality. New emerging technologies play a significant role in various aspects of sugarcane breeding programs. The major ones include clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9), machine learning (ML), artificial intelligence (AI), metabolic engineering, RNA interference (RNAi), bioinformatics, nanotechnology, etc. Advanced molecular breeding tools and biotechnological methodologies are now available for targeting pest- and disease-resistant traits in sugarcane. The chief focus of this chapter is on the usefulness of a target gene/metabolite/computational approach for improving sugar yield using the abovementioned emerging technologies.