Optimizing light interception efficiency in crop canopies represents a critical frontier in sustainable intensification of agricultural systems. Traditional seed spacing practices rely on fixed empirical recommendations that fail to account for genotype-specific morphology, dynamic environmental conditions, and spatial heterogeneity in field-scale production systems. This limitation results in suboptimal radiation use efficiency, reduced photosynthetic capacity, and yield gaps across diverse cropping environments. Artificial intelligence and machine learning technologies offer transformative potential for developing adaptive, data-driven seed spacing strategies that maximize canopy light interception while minimizing resource inputs. This review examines the integration of deep learning architectures, optimization algorithms, and crop growth simulation models to predict optimal planting geometries based on plant architectural traits, solar radiation patterns, and agronomic objectives. Advanced phenotyping platforms, including unmanned aerial vehicles, ground-based sensors, and three-dimensional canopy reconstruction systems, provide the spatiotemporal data streams necessary for training and validating predictive models. Field implementations across row crops, cereals, and horticultural systems demonstrate measurable improvements in radiation use efficiency, biomass accumulation, and yield stability under variable climatic conditions. The convergence of reinforcement learning, Bayesian optimization, and real-time decision support systems enables precision planting equipment to execute variable-rate seed spacing prescriptions at field scale. Despite promising advances, challenges remain in model generalization, computational scalability, and integration with autonomous agricultural machinery. Future research must address data scarcity in underrepresented cropping systems, develop robust uncertainty quantification frameworks, and establish industry standards for AI-driven agronomic decision-making.