With rapid advancements in nanopore technology, research on single-biomolecule identification using nanopores has been significantly expedited. Nanopore sensing is based on detecting the change in ionic current in a nanopore as a molecule traverses through it. Single-biomolecule identification, with nanopores, involves two key steps. First, detecting the start and end of biomolecule translocations (termed as events) and second, extracting features from these events to uniquely identify the molecules. Robust event detection is critical as incorrect or partial event detection can increase computational load and hinder correct identification of biomolecules. This article reviews the state-of-the-art (SOTA) for event detection, starting from simple models to more sophisticated, stochastic approaches. Following a discussion on the core methodology, the algorithms are benchmarked on common, real datasets. The strengths, limitations, and tradeoffs for each algorithm are highlighted—which can serve as a guide for interested researchers in the field to help select and tune suitable algorithms for their use-cases.