Over the past four decades, X-ray fluorescence based analytical techniques have been deeply employed for a vast range of applications that extend from academic to industrial settings. Among these applications, the field of environmental toxicology has relied in XRF as a trustworthy and sensitive tool for purposes such as geological studies of soil and sediments, vegetable analyses, air and water pollutants characterization and waste management. Adding to its non-destructive nature and lack of sample preparation, large quantities of data are produced during XRF analyses. In this way, suitable methodologies for data processing and interpretation are necessary and newer approaches are under constant demand. This chapter aims to review the current tools and future trends used to statistically process data previously collected through XRF devices in the field of environmental toxicology. The main advantages and disadvantages of each technique are equally addressed. Finally, newer mathematical models whose preliminary results denote an auspicious future, namely, recurrent, deep and convolution neural networks-based algorithms, are described and properly framed in the environmental toxicology field.