Summary Species classification is a practice that has gained popularity over the last few years across various disciplines, especially in natural sciences. The classification is vital in that it helps us understand diversity and facilitate the proper identification of different organisms. Categorizing organisms using the classification method has also proven critical in better understanding complex organisms’ evolution. Technological innovation has made it easier for scientists to classify organisms in various ways. One of the technological innovations that have played a significant role in facilitating taxonomy is data mining, allowing scientists to extract and apply essential data about species using mathematical, statistical, and artificial intelligence methods. Technology provides easy access to biological information about species, demonstrating their structural differences between the past and the future. Unlike human intelligence which is highly prone to mistakes, data mining is more accurate, reliable, and easy to use when communicating biological information. Mining data sets regarding the classification of iris flowers species will be essential in documenting the past, present, and future evolution of the flowers while providing an effective method of communicating this information to future scientists. Data mining will also prove critical in helping scientists understand the structural changes of the flower in its efforts to adapt to the changing information. Problems and Opportunities Data mining is increasingly becoming a common and valuable practice among scientists in different fields. The concept refers to the practice of exploring large blocks of data sets using machine learning techniques to extract data patterns, trends, and statistics (Roiger, 2017). The practice aims at discovering only the most meaningful patterns and trends to determine outcomes. Sowmya and Suneetha (2017) also defined data mining as how one identifies anomalies and correlations to predict an outcome. Therefore, the data that researchers discover using data mining processes must be usable and significant to the overall process. Data mining regarding the classification of iris flowers will be important in identifying the existing iris species, outlining the structural differences between the various species, identifying the possible causes for the differences, and underpinning potential future changes and causes of the flower’s evolution. The iris flower is a genus that consists of various species with distinctive features that set them apart from other similar-looking flowers. However, pattern recognition through speech and images remains a significant limit in the flower’s classification (Choi et al., 2019). The availability of machine learning has facilitated the classification process. Scientists experienced breakthroughs when deep neural networks became applicable to extracting external features from labeled data (Choi et al., 2019). The usefulness of the neural networks in iris flower classification proves that data mining has been crucial in enhancing the classification of the flowers’ species. Data mining will identify these different species and their distinctive features and structures while facilitating standard scientific naming. Some botanists say tha