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Ensuring fire safety in residential buildings remains a critical component of sustainable urban development, particularly in developing countries. This research addresses the critical fire hazards in Kabul, Afghanistan, where rapid, informal urban expansion (comprising 70–80% of the housing stock), high population density, and unstable electrical grids combined with non-standard winter heating have historically led to severe casualties and multi-million-dollar economic losses. The traditional fire alarm and manual suppression methods currently prevalent in Kabul suffer from long detection delays, high false alarm rates, and heavy reliance on limited local resources, creating an urgent need for advanced, automated interventions. The primary objective of this study is to evaluate the applicability and effectiveness of Intelligent Fire Protection Systems (IFPS) integrated with the Internet of Things (IoT) and smart automation within typical multi-story residential buildings in Kabul. A comprehensive analysis of Kabuls demographic and technical fire data from the 2010–2025 period was conducted. Using a three-dimensional field (CFD) model developed in Fire Dynamics Simulator (FDS) and visualized via Smokeview, multiple computational experiments were performed on a typical six-story residential building layout under severe fire scenarios, including apartment and staircase ignition sources. The simulation results revealed that under conventional conditions (without automated systems), the Available Safe Evacuation Time (ASET) for a fast-growing fire is restricted to 220–250 seconds for an apartment fire, and drops critically to 150–180 seconds for a staircase fire due to rapid vertical smoke propagation via the "chimney effect". Concurrently, the Required Safe Evacuation Time (RSET) modeled via agent-based simulation for 150–200 occupants ranged between 300–400 seconds, fundamentally violating the safety criterion (ASET > RSET) and matching historical local fire dynamics. However, alternative simulation scenarios implementing intelligent automated sensors and localized smart sprinkler systems demonstrated a 40–50% reduction in the maximum Heat Release Rate (HRR) and extended the ASET window by 100–150 seconds, successfully validating the safety parameters. Based on these findings, this study proposes an adapted, cost-effective, and autonomous low-power framework for intelligent fire safety using multi-sensor detectors and low-power decentralized alert networks tailored to withstand Kabuls power supply constraints. The practical recommendations provided herein serve as a technical roadmap for the National Emergency Management Administration and civil developers to upgrade regional construction codes and systematically reduce urban vulnerability to fire hazards.