Understanding defects in perovskite solar cells (PSCs) is crucial for improving their efficiency and stability. Lead halide perovskites, despite their high performance, suffer from intrinsic defects that act as recombination centers, limiting device efficiency. Computational studies, particularly first-principles calculations, are essential for understanding defect chemistry. However, the complex defect structure and soft lattice of perovskites pose challenges. This review highlights current knowledge and future directions in defect research, emphasizing the need for advanced computational methods and data-driven approaches to design strategies for better PSC performance. It also stresses the importance of linking theoretical studies with experimental investigations to provide insights for both scientific and industrial communities. The efficiency of PSCs has increased significantly, but challenges remain, including recombination losses, poor stability, and phase transitions. Defects such as vacancies, interstitials, and antisites contribute to these issues. Understanding defect formation energy, single-electron levels, and thermodynamic transition levels is critical for assessing their impact on performance. Defect concentrations and recombination rates are also important factors. Recent studies have shown that defects can be shallow or deep, with deep defects being more detrimental. Theoretical calculations, while useful, often lack quantitative consistency with experimental results due to computational limitations and the complexity of defect interactions. Defect tolerance in perovskites is a concept that suggests certain defects do not significantly affect performance. However, this is not universally true, as some defects can act as recombination centers. The soft lattice of perovskites allows for significant lattice fluctuations, affecting defect properties. Lattice expansion and compression can influence defect levels and carrier recombination. Defect diffusion, often linked to hysteresis and instability, requires further investigation. Extended defects such as grain boundaries, interfaces, and interphase boundaries also play a role in defect behavior. Passivation of defects at these interfaces is an active area of research. Defect-triggered phase transitions and degradation are significant challenges, with defects accelerating phase changes. Strategies to enhance phase stability include passivating defects and introducing non-intrinsic defects. High-throughput computing and machine learning are promising tools for accelerating defect research, enabling faster predictions and insights into defect properties and their impact on PSC performance. These methods can help identify optimal passivators and design strategies for stable, efficient PSCs.Understanding defects in perovskite solar cells (PSCs) is crucial for improving their efficiency and stability. Lead halide perovskites, despite their high performance, suffer from intrinsic defects that act as recombination centers, limiting device efficiency. Computational studies, particularly first-principles calculations, are essential for understanding defect chemistry. However, the complex defect structure and soft lattice of perovskites pose challenges. This review highlights current knowledge and future directions in defect research, emphasizing the need for advanced computational methods and data-driven approaches to design strategies for better PSC performance. It also stresses the importance of linking theoretical studies with experimental investigations to provide insights for both scientific and industrial communities. The efficiency of PSCs has increased significantly, but challenges remain, including recombination losses, poor stability, and phase transitions. Defects such as vacancies, interstitials, and antisites contribute to these issues. Understanding defect formation energy, single-electron levels, and thermodynamic transition levels is critical for assessing their impact on performance. Defect concentrations and recombination rates are also important factors. Recent studies have shown that defects can be shallow or deep, with deep defects being more detrimental. Theoretical calculations, while useful, often lack quantitative consistency with experimental results due to computational limitations and the complexity of defect interactions. Defect tolerance in perovskites is a concept that suggests certain defects do not significantly affect performance. However, this is not universally true, as some defects can act as recombination centers. The soft lattice of perovskites allows for significant lattice fluctuations, affecting defect properties. Lattice expansion and compression can influence defect levels and carrier recombination. Defect diffusion, often linked to hysteresis and instability, requires further investigation. Extended defects such as grain boundaries, interfaces, and interphase boundaries also play a role in defect behavior. Passivation of defects at these interfaces is an active area of research. Defect-triggered phase transitions and degradation are significant challenges, with defects accelerating phase changes. Strategies to enhance phase stability include passivating defects and introducing non-intrinsic defects. High-throughput computing and machine learning are promising tools for accelerating defect research, enabling faster predictions and insights into defect properties and their impact on PSC performance. These methods can help identify optimal passivators and design strategies for stable, efficient PSCs.