The simple model organism C. elegans provides an ideal platform for addressing critical questions in neuroscience, developmental biology, and systems biology. By tracing the cell lineage --- the developmental history of cells in C. elegans --- researchers can uncover profound insights into cellular behaviors such as division, migration, fate differentiation, signaling, and gene expression patterns, with significant implications for understanding higher organisms, including humans. The most common approach for lineage tracing in C. elegans is cell tracking. However, this approach has notable limitations, including resource inefficiency, computational intensity, and the need for extensive manual labor as it involves processing all imaging frames back to the four-cell stage of embryonic development and demands time-consuming manual annotation and correction. In this thesis, I address the challenges of cell lineage tracing --- or equivalently, the problem of cell recognition --- from novel perspectives, proposing two innovative methods to overcome these limitations with machine learning. First, we demonstrated that nuclei can be accurately detected and segmented throughout 3D+t imaging sequences of C. elegans embryos using a well-trained deep learning model. Building on this, we approached the cell recognition problem using point-cloud representations of nuclei, rather than raw images. Leveraging the invariant lineage of C. elegans, we developed two methods for cell recognition in pre-twitching embryos that require less information and significantly reduce computational costs and resources compared to cell tracking approaches. The first method is a machine learning-based cell classification approach that predicts cellular identities using spatiotemporal features from a cell's available frames. This approach achieved exceptional performance, exceeding 91% accuracy, and directly predicts cell names. The second method is an atlas/template-based cell recognition approach that identifies cells from a single image frame using point-cloud registration. This method achieved high accuracy >= 80% for most pre-twitching time points without looking at any additional image frames or requiring extra information. These two methods open new avenues for cell recognition in C. elegans embryos beyond cell tracking, offering significant improvements in efficiency and applicability. We envision that these methods will benefit the C. elegans research community and inspire further advancements in the field.