In this work, we put forward inTIME, a machine learning-based integrated framework that provides an holistic view in the cyber-threat intelligence process and allows security analysts to easily identify, collect, analyse, extract, integrate, and share cyber-threat intelligence from a wide variety of online sources including clear/deep/dark web sites, forums and marketplaces, popular social networks, trusted structured sources (e.g., known security databases), or other datastore types (e.g., pastebins). Staying on top of these evolving cyber-threats has become an increasingly difficult task that presently relies heavily on collecting and utilising cyber-threat intelligence before an attack (or at least shortly after, to minimize the damage) and entails the collection, analysis, leveraging and sharing of huge volumes of data. The sheer volume and variety of the different operating systems, the device particularities, the various usage domains and the accessibility-ready nature of the platforms creates a vast and complex threat landscape that is difficult to contain. In today’s world, technology has become deep-rooted and more accessible than ever over a plethora of different devices and platforms, ranging from company servers and commodity PCs to mobile phones and wearables, interconnecting a wide range of stakeholders such as households, organizations and critical infrastructures.