Perovskite solar cells (PSCs) have emerged as a promising alternative to traditional silicon solar cells due to their high absorption coefficient, low production cost, flexibility, and tunable transparency. Despite these advantages, the efficiency of PSCs is heavily influenced by the physicochemical properties of the perovskite material, including its crystalline structure, chemistry, and morphology. These characteristics significantly affect key performance parameters such as short-circuit current density (Jsc), open-circuit voltage (Voc), fill factor (FF), and overall efficiency (η). Integrating machine learning (ML) in materials science has shown the potential to advance the design and manufacturing of PSCs by offering more accurate assessments of these factors. ML algorithms can identify complex patterns in experimental and simulated data, enhancing the development process by reducing time and costs and fostering innovation in new material compositions and structures. This paper provides a comprehensive review of state-of-the-art ML methods applied to PSC performance prediction. It highlights the role of data-driven models, explainable artificial intelligence (X-AI), and physically informed machine learning (PIML) in modeling and predicting PSC behavior. While ML techniques offer promising advancements, the review identifies challenges such as the limited integration of fundamental physical knowledge into data-driven models, the need for more research on the stability of PSCs using ML tools, and the potential of generative models to explore new configurations of perovskite materials. Addressing these challenges could lead to more accurate, efficient, and stable PSCs, paving the way for their commercial viability.