Haulage is frequently one of the largest cost, energy, equipment, and safety exposures in surface mining. It is also one of the most complex operating systems to manage. A haulage operation is influenced by pit geometry, route design, road condition, grades, payload distribution, material density, shovel performance, dispatch decisions, operator behaviour, maintenance availability, weather, traffic interaction, tyre condition, fuel quality, sensor reliability, and production targets. Because these variables interact continuously, performance improvement requires more than local optimization of trucks or dispatch rules. It requires a systems approach. This book provides that systems approach. It presents haulage improvement as an integrated mining engineering discipline that combines operational excellence, road engineering, data governance, fleet analytics, safety management, maintenance reliability, energy management, and business value realization. The manuscript will guide readers from the fundamentals of haulage systems through to advanced methods such as machine learning, simulation, digital twins, autonomous haulage, electrification, and low-carbon operating strategies. The book is written for practitioners who need to make better decisions with imperfect data and real operational constraints. It explains how to structure cycle-time diagnostics, classify delays, interpret payload performance, evaluate queueing, identify road-related losses, reconcile fuel data, assess truck health, and link improvement opportunities to financial outcomes. It emphasizes that data and analytics are valuable only when they improve operational decisions, frontline routines, and accountable execution. A major theme of the book is the importance of reliable measurement. Many haulage improvement programs fail because the available data is incomplete, inconsistent, poorly governed, or disconnected from the realities of the mine. The book therefore devotes significant attention to fleet management systems, telemetry, sensor quality, timestamp alignment, data lineage, master data, KPI definitions, and cross-functional governance. This makes the book especially relevant to modern mines investing in digital transformation and AI-enabled operations.