Cost estimation for electrical building services becomes increasingly difficult as Saudi construction programmes combine compressed procurement schedules, evolving designs, complex low-current systems and volatile supplier markets. This integrative review examines how artificial intelligence can augment electrical cost estimation and budget optimization without displacing professional engineering judgment. It differentiates estimating accuracy from commercial feasibility and explains why models trained on generic building costs are not automatically transferable to medium-voltage distribution, low-voltage networks, structured cabling, security installations or building automation. The review synthesizes recent construction cost prediction scholarship, Saudi evidence on cost drivers, and related work on digital project information. Its central contribution is a governance-oriented framework integrating a controlled electrical work-breakdown structure, validated historical quantities, contextual price features, interpretable prediction models, procurement risk scenarios and accountable estimator approvals. The analysis finds that predictive algorithms offer substantial promise for early budget range development and tender comparison, yet their reliability depends on scope normalization, temporal validation, uncertainty communication and auditable changes. Practical implementation should begin with narrow trade-specific pilots and progress towards integrated cost intelligence once data quality and organizational safeguards are demonstrated. The study concludes with research priorities for prospective evaluation within Saudi infrastructure programmes.