International last-mile delivery—the final stage of a global logistics journey—faces complex, region-specific challenges involving customs clearance, fragmented transport networks, urban congestion, differing postal infrastructures, and regulatory variations. Algorithms increasingly optimize this stage using AI-driven routing, dynamic clustering, real-time fleet allocation, crowdsourced delivery models, and multimodal transport decision systems. This study analyzes algorithmic models enabling efficient last-mile delivery across borders, applying sample datasets across five regions. Results indicate that optimal performance emerges from hybrid algorithms integrating machine learning, geospatial intelligence, blockchain tracking, and local partner routing models. Future frameworks must balance cost, sustainability, speed, and consumer expectations while adapting to varied infrastructure maturity worldwide.