The underlying reasons behind modern terrorism are seemingly complex and intangible. Despite diverse causal mechanisms, research has shown that there exists general statistical patterns at the global scale that can shed light on human confrontation behaviour. Whilst many policing and counter-terrorism operations are conducted at a city level, there has been a lack of research in building city-level resolution prediction engines based on statistical patterns. For the first time, the paper shows that there exists general commonalities between global cities under terrorist attacks. By examining over 30,000 geo-tagged terrorism acts over 7000 cities worldwide from 2002 to today, the results shows the following. All cities experience attacks $A$ that are uncorrelated to the population and separated by a time interval $t$ that is negative exponentially distributed $\sim \exp(-A^{-1})$, with a death-toll per attack that follows a power law distribution. The prediction parameters yield a high confidence of explaining up to 87\% of the variations in frequency and 89\% in the death-toll data. These findings show that the aggregate statistical behaviour of terror attacks are seemingly random and memoryless for all global cities. The enabled the author to develop a data-driven city-specific prediction system and we quantify its information theoretic uncertainty and information loss. Further analysis show that there appears to be an increase in the uncertainty over the predictability of attacks, challenging our ability to develop effective counter-terrorism capabilities.