Increasingly relevant, for instance, is the necessity to propose sustainable solutions for the display and storage of artwork that account for energy consumption [21]. Also, there is a critical need to balance safe access to cultural heritage with sustainable tourism; this stimulates reflections on how best to measure and define ideal housing and display conditions for each cultural artifact based on its material type. Conservation scientists whose work focuses on preventive conservation are currently tackling how best to address these issues. Another area of significant effort in the field is integrating artificial intelligence (AI) and machine learning methodologies into currently adopted techniques, including macro-XRF (MA-XRF) and micro-computational tomography (micro-CT), to enhance spectral information by significantly improving detection limits, chemical sensitivity, and spatial resolution [22; 23]. Using big data to predict the behavior of art over long periods of time is also an area ripe for inquiry [24]. As with the rest of the scientific community, finding applications in the field that harness the capabilities of AI and pattern recognition (PR) is a developing research area [25]. Systematic work focused on addressing applied conservation efforts is generally less frequent in the conservation science field. However, relevant publications in this area have involved, among other subjects, the synthesis of nanostructured gels and microemulsions with various formulations as green products for consolidation and cleaning [26]. In terms of monitoring and evaluating conservation interventions, several analytical methods can provide a snapshot of the object's condition at a certain treatment stage; at present, however, no scientific tool can yield real-time information on the progress of interventions. Ultimately, collaboration will remain a key aspect of cultural heritage research, from technological and methodological innovation to the technical study and conservation of artifacts. The development of collaborative programs, both in Europe and the United States, has created opportunities for smaller institutions without science laboratories or scientists on staff to access state-of-the-art analytical instrumentation and highly specialized expertise to carry out in-depth analysis at shared facilities or conduct scientific investigations of previously unexplored art objects [27; 28]. Thanks to the availability of funding to support these activities within these same programs and the advent of miniaturized equipment, this growing model ensures both economic and environmental sustainability and logistical feasibility for the operators and users. In this context, this Special Issue aims to illustrate relevant recent advances in the field of analytical methods for cultural heritage while addressing some of the outstanding gaps that have been identified over time. Topics covered include the optimization of various imaging technologies for enhanced materials identification in complex artifacts using neural networks and for the digitization of surface data from objects located in constrained environments; the implementation of a multi-analytical approach, featuring cutting-edge mass spectrometry-based proteomics and chromatographic methods, to shed new light on understudied African collections and contemporary art installations; a study of materials' properties for possible incorporation in cleaning practice, as well as the implementation of analytical techniques and methodologies for the identification of degradation products within specific types of materials and for the evaluation of conservation interventions; and a review of sampling techniques used in preventive conservation for the monitoring of indoor air quality in heritage environments. No external funding was received to develop this manuscript. The authors declare no conflict of interest.