Next-generation sequencing (NGS) has revolutionized the process of scanning multiple fragmented DNA molecules simultaneously and sequencing them in parallel. The large amount of data generated needs to be analyzed carefully to extract important information in the genomes. Bioinformatics algorithms and tools have been developed in response to this challenge, enabling the large-scale analysis of NGS data. Such tools help in determining the quality of the sequence reads, alignment of the reads against reference genomes or de novo assembly, identifying the variants, annotating these variants with a vast array of datasets, and further analyses specific to the problem at hand. Bioinformatics has evolved along with the evolution of NGS technologies, for example, from short-read to long-read sequencing. NGS technologies, with the help of bioinformatics, have supported the development of genomics including disease-targeted sequencing, high-throughput metagenomics, and precision medicine implementation. Recent advances in NGS, including technologies such as single-cell sequencing and long-read sequencing, enhance the breadth and accuracy of genetic investigations to uncover the molecular causes of various diseases. Artificial intelligence (AI) and machine learning (ML) promise a future in which genomics not only informs healthcare but also predicts the risk of developing diseases, provides accurate diagnosis and treatment options, as well as predicts the outcome of the disease and response to treatment.