Fluorescence lifetime imaging microscopy (FLIM) has emerged as a promising tool for all scientific studies in recent years. However, the utilization of FLIM data requires complex data modeling techniques, such as curve-fitting procedures. These conventional curve-fitting procedures are not only computationally intensive but also time-consuming. To address this limitation, machine learning (ML), particularly deep learning (DL), can be employed. This review aims to focus on the ML and DL methods for FLIM data analysis. Subsequently, ML and DL strategies for evaluating FLIM data are discussed, consisting of preprocessing, data modeling, and inverse modeling. Additionally, the advantages of the reviewed methods are deliberated alongside future implications. Furthermore, several freely available software packages for analyzing the FLIM data are highlighted.