With the gradual technological changes in the current era, data science using soft computing and machine learning methods has played a tremendous role in various areas, including the biomedical and health sectors. The etymology of bioinformatics with several types of omics, such as genomics, transcriptomics, proteomics, and metabolomics was coined in the late twentieth century in the direction of the investigation and finding solutions for informatics processing in biological decision-making systems. Similarly, the data science era began and played a vital role in solving a large domain, particularly in biological problem solutions. In this conceptual article, an integrated machine learning-based framework for the prediction of outbreaks is proposed, followed by omics biological data processing and dimension reduction. Subsequently, various prominent benchmark tools have been shown related to problem solutions in data science and bioinformatics applications. In this study, the role of data science and soft computing approaches in bioinformatics disciplines with various associated applications is explored. Various simple soft computing techniques such as fuzzy logic, artificial neural networks, support vector machines and evolutionary computation techniques have been explained. Furthermore, complex optimization and dimension reduction techniques, such as artificial bee colony, ant colony optimization, formal concept analysis, and principal component analysis have been presented. Finally, active research zones in bioinformatics, as well as their current trends and challenges have been highlighted which will lead to future research directions.