Revenue forecasting underpins fiscal planning and macro-economic policy, yet no consensus exists on which modelling paradigm,
data source, or evaluation protocol delivers the most reliable projections. This paper provides a systematic review of the revenue-
forecasting literature from 1970 to 2024, synthesising 312 peer-reviewed studies and 48 institutional reports across national, sub-
national, and sector-specific jurisdictions. We taxonomise approaches into six families: (1) univariate time-series, (2) multivariate
macro-econometric, (3) micro-simulation and tax-buoyancy models, (4) machine-learning and deep-learning systems, (5) judgmental
and hybrid ensembles, and (6) now-casting and real-time frameworks leveraging high-frequency data. For each family we summarise
theoretical foundations, typical covariates, estimation techniques, and reported forecast-error metrics. Meta-analysis of 1,174
reported forecast horizons shows that hybrid methods combining macro-econometric structure with machine-learning adjustments
reduce mean absolute percentage error (MAPE) by 15–30 % relative to pure time-series benchmarks at horizons up to two years.
Conversely, gains dissipate beyond three years unless judgmental overrides are incorporated. We highlight emerging
practices—vector-based tax-gap modelling, transformer architectures with fiscal attention layers, and narrative-augmented now-
casting—that exploit novel data such as satellite imagery and web-scraped prices. The review also identifies persistent gaps: limited
out-of-sample testing, inconsistent uncertainty quantification, and scarce cross-country transferability studies. We close by proposing
a reproducibility checklist and an open repository schema to accelerate comparative research and policy uptake.